ACKNOWLEDGEMENTS

This research is funded by UNESCO/MAB Young Scientist Award grant number SC/EES/AP/565.19, particularly from the Austrian MAB Committee as part of the International Year of Biodiversity. I would like to extend my deepest gratitude to the Man and Biosphere-LIPI (Lembaga Ilmu Pengetahuan Indonesia) which was led by Prof. Endang Sukara (President of the MAB National Committee) and at present is substituted by Prof. Dr. Bambang Prasetya, Dr. Yohanes Purwanto (MAB National Committee) for endorsing this research, and Sri Handayani, S.Si. (MAB National Staff), also especially to the Mount Gede Pangrango National Park for allowing to work at Selabintana and Cisarua Resort, and the Carbon team members: Ahmad Jaeni, Dimas Ardiyanto, Eko Susanto, Mukhlis Soleh, Pak Rustandi and Pak Upah. Dr. Didik Widyatmoko, M.Sc., the director of Cibodas Botanic Garden for his encouragement and constructive remarks, Wiguna Rahman, S.P., Zaenal Mutaqien, S.Si. and Indriani Ekasari, M.P. my best colleagues for their discussions. Prof. Kurniatun Hairiah and Subekti Rahayu, M.Si. of World Agroforestry Center, M. Imam Surya, M.Si. of Scoula Superiore Sant’ Anna Italy also Utami Dyah Syafitri, M.Si. of Universiteit Antwerpen Belgium for intensive discussion, Mahendra Primajati, S.Si. of the Burung Indonesia for assisting with the map and Dr. Endah Sulistyawati of School of Life Sciences and Technology - Institut Teknologi Bandung for the great passion and inspiration.

i TABLE OF CONTENTS

Page

LIST OF TABLES iii

LIST OF FIGURES iv

EXECUTIVE SUMMARY vi

1.0 INTRODUCTION 1

1.1 Objectives 3

2.0 METHODS 4

2.1 St udy site 4

2.2 Measurement of carbon stock and estimating the biomass 6

2.3 Plant diversity 8

2.4 Relationship between carbon stock and plant diversity 9

2.5 Carbon value 9

2.6 Ecosystem service 9

3.0 RESULTS AND DISCUSSION 10

3.1 Estimation carbon stock and biomass 10

3.2 Plant diversity as the carbon stock performance and its conservation value 15

3.3 Relationship between carbon stock and plant diversity 32

3.4 Carbon stock and plant diversity as basis of an ecosystem services model 41

4.0 CONCLUSION 44 REFERENCES 45 APPENDICES 49

ii LIST OF TABLES

Page

Table 1. Average carbon stocks for various biomes 1

Table 2. The main ecological zone of Mount Gede Pangrango National Park 4

Table 3. All ometric models used to convert measures of vegetation to AGB 6

Table 4. Th e average carbon stock in observation plot at 3 zones of MGPNP 11

Table 5. Th e average contribution of carbon stock from trees on 4 allometric equations 13

Table 6. Eco logical index of 30 species with highest importance value on study site 16

Table 7. C lass and category of tree density 18

Table 8. Rela tive value of ecological index component on Mount Gede Pangrango National Park 22

Table 9. Ca rbon stock, ecology indexes and formation at the subalpine zone 32

Table 10. Ca rbon stock, ecology indexes and formation at the montane zone 34

Table 11. Ca rbon stock, ecology indexes and formation at the submontane zone 35

Table 12. Bra y-Curtis Index 36

Table 13. H ighest ten species in carbon stock value on 4 allometric equations 37

Table 14. Th e approach of interval value test for carbon stock on the model of cubic polynomial regression 40

Table 15. Est imation of carbon stock on the nature forest MGPNP 41

iii LIST OF FIGURES

Page

Figure 1. An egg of well-being 3

Figure 2. Study site map on Mount Gede Pangrango National Park 5

Figure 3. Sampling plot for carbon measurement made with length direction in line with elevation line with the assumption representing vegetation gradation on each elevation 6

Figure 4. Est imation of carbon stock and biomass on several elevations, AGB is aboveground biomass that consist of trees, understorey, litter and necromass 10

Figure 5. Th e value of trees proximity based on the elevation zone. 11

Figure 6. Ca rbon stock is stored in two classes of tree sizes on different elevations 12

Figure 7. Co ntribution of tree component, understorey, litter and necromass for carbon stock on different elevations 14

Figure 8. Co mposition and abundance of plant species on 3 ecology zones MGPNP 17

Figure 9. Th e relationship between species density rank and its abundance 18

Figure 10. Co mposition and abundance of plant species on subalpine zone 19

Figure 11. Co mposition and abundance of plant species on montane zone 20 MGPNP

Figure 12. Co mposition and abundance of plant species on submontane zone of MGPNP 21

Figure 13. Th e relationship of basal area, density and importance value to carbon stock on the elevation of 2802 m asl 23

Figure 14. Th e relationship of basal area, density and importance value to carbon stock on the elevation of 2601 m asl 24

Figure 15. Th e relationship of basal area, density and importance value to carbon stock on the elevation of 2329 m asl 25

Figure 16. Th e relationship of basal area, density and importance value to carbon stock on the elevation of 2075 m asl 26

iv Page

Figure 17. Th e relationship of basal area, density and importance value to carbon stock on the elevation of 1851 m asl 27

Figure 18. Th e relationship of basal area, density and importance value to carbon stock on the elevation of 1710 m asl 28

Figure 19. Th e relationship of basal area, density and importance value to carbon stock on the elevation of 1355 m asl 29

Figure 20. Th e relationship between basal area, density and importance value to carbon stock on the elevation of 1271 m asl 30

Figure 21. Th e relationship between basal area, density and importance value to carbon stock on the elevation of 1079 m asl 31

Figure 22. Ca rbon stock of plant species at the observation plot on Mount Gede Pangrango National Park 38

Figure 23. Mo del of cubic polynomial regression for correlation between Shannon Index and carbon stock on 4 allometric equations 39

Figure 24. Eco system services model on Mount Gede Pangrango National Park based on carbon stock and plant diversity 42

v EXECUTIVE SUMMARY

Mount Gede Pangrango National Park (MGPNP) is a wet-climate mount ecosystem that is rich for its plant diversity and has a significant contribution in storing carbon stock in its biomass and in time, it should be an ecosystem service. The purpose of this research is (1) to obtain information and data of carbon stock in relation to the plant diversity on nature forest ecosystem of Mount Gede Pangrango National Park as the core zone of Cibodas Biosphere Reserve (2) to formulate ecosystem services model on the wet climate mountains in West Java based on the strong linkage between carbon stock and plant diversity, and (3) to support scientific data in welcoming REDD/REDD plus, thus the result will directly contribute to the multistakeholder in Cibodas Biosphere Reserve for participating in reserving the main site. The method of measurement consists of (1) dividing Mount Gede Pangrango National Park in its each unique ecosystem as follows: (a) submontane zone (1000-1500 m asl), (b) montane zone (1500-2400 m asl), and (c) subalpine zone (2400-3019 m asl); (2) measuring the carbon stock above and below ground by using World Agroforestry Center (2007) as a guide; (3) estimating the biomass of branched trees by applying the equation of Dry Weight/DW as follows: (a) DW = 0,118 D2,53(Brown, 1997), (b) DW = 0,11 ρ D2,62 (Ketterings et al., 2001), (c) AGB est= ρ x exp(-1,499 + 2,148ln(D) + 0,207(ln(D))2-0,0281(ln(D))3) (Chave et al., 2005), (d) Ln (TAGB) = c + αln(DBH) (Basuki et al., 2009). The estimation of the biomass of non-branched trees uses DW = π ρ HD2/40 (Hairiah et al., 2001); (4) calculating plant diversity by using quantitative parameter, including index of importance value, index of diversity and index of similarity; (5) analyzing the relationship between carbon stock and plant diversity by using excel program on Mac OS X Version 10.5.8 and model test of polynomial regression on the confidence level of 95%; and (6) calculating of carbon value based on the ecological zone ability in supporting carbon. The average result of carbon stock measurement (aboveground carbon stock) on 9 sites using four allometric equations in tons C per hectare is 399.717 (Brown/Br), 354.648 (Ketterings/Kt), 449.688 (Chave/Cv) and 325.724 (Basuki/Bs). The estimated biomass is assumed that its 46% is stored carbon stock which is as much as 868.950 (Br), 770.974 (Kt), 977.583 (Cv) and 708.095 (Bs). The highest carbon stock is found on 2329 m asl (montane zone), except Bs allometric on 2601 m asl (subalpine zone), and the lowest one is found on 1355 m asl (Br) and 1079 m asl (Kt, Cv, Bs). The average of highest carbon stock based on zone is on the subalpine zone, that is 409.751 (Kt), 553.858 (Cv),

vi 411.661(Bs) except Br (463.466) on the montane zone. It is presumably caused by contribution of Vaccinium varingiaefolium, which is multistem as well as dominant species on the subalpine zone. The maximum proximity value from the big trees to the small ones happens on the subalpine zone, that is 280 individuals ha-1 of the big tree (2601 m asl) and 2950 individuals ha-1 (2802 m asl). Meanwhile the approximate average of contribution on the aboveground carbon stock from the big trees 42.697-51.963% and from the small trees 29.664-35.892%. The tree component has the significant average contribution value to the total carbon stock, that is 82.803% (Br), 80.692% (Kt), 84.994% (Cv) and 79.766% (Bs). It shows the role of tree biomass is highly essential in supporting carbon. On the other side, the average contribution on the aboveground carbon stock from understorey is as much as 1.302% (Br), 1.517% (Kt), 1.198% (Cv) and 1.559% (Bs); from litter is 12.895% (Br), 14.83% (Kt), 11.778% (Cv) and 15.735% (Bs); and from necromass is 3.308% (Br), 3.932% (Kt), 3.156% (Cv) and 4.117% (Bs). It is known that there are 13 top species with the highest carbon stock approximately between 60.159-772.624 tons C ha-1, those are Schima wallichii, Vaccinium varingiaefolium, Castanopsis tungurrut, Lithocarpus sundaicus, Leptospermum flavescens, Platea latifolia, Myrsine hasseltii, Toona sureni, Symplocos odoratissima, Neolitsea cassiaefolia, Castanopsis javanica and Cyathea junghuhniana. The high value of carbon stock on this research is a reflection of species dominance from all elevations which carbon stock is measured. It is an interesting fact that Schima wallichii is found from 1271 (submontane) until 2601 m asl (subalpine) which is shown with a high frequency of 0.778 and importance value of 36.865. This is why eventually Schima wallichii has the highest carbon stock value. Meanwhile, the crater species, Vaccinium varingiaefolium has the highest importance value 49.152 with the width of basal area 314.714 m2 ha-1, even though with small frequency of 0.222. Furthermore, it is important to note that this crater plot has the lowest Shannon index, which is 1.409. Based on tree proximity class per hectare, it shows that its 68.182% is classified into class occasionally (0-50), and its 25.76% is classified into class often (51-150) and the rest is class abundant (100-450) that has relatively small proportion, that is 6.06%. On subalpine zone, there are abundant of species on wet-climate mountains such as Vaccinium varingiaefolium, Symplocos odoratissima, Myrsine hasseltii, and Leptospermum flavescens. Montane zone is the widest area than two other zones and there are species with the highest number in the area, such as Schima wallichii,

vii Lithocarpus sundaicus, Platea latifolia, Neolitsea javanica and Elaeocarpus angustifolius. The highest number on the submontane zone are Schima wallichii, Castanopsis tungurrut, Oriocnide scabra, Castanopsis javanica and Phoebe grandis. The highest number of species is often found at Selabintana V (1710 m asl) that is 25, then Pasir Ipis (1355 m asl) that is 23, thus, both of them have relatively higher Shannon Index than other elevations, which are 2.925 and 2.701. Generally, all zones have similar tendency, which is they have positive and significant correlation between ecology indexes (basal area, density, importance value) to the carbon stock. The different ability in storing carbon at each zone is presumably related to the level of tree proximity per hectare and number of species, because in time it will affect the ecology indexes value. Tree proximity value per hectare accordingly from subalpine to submontane is 93, 38 and 17; meanwhile the number of species is 16, 70 and 44. On the subalpine zone, the correlation value (R2) is found to be very high. The average is 0.971 on basal area parameter and 0.991 on the importance value; meanwhile the average for density is 0.768. These values are higher than the ones on the montane and submontane zone. According to Bray-Curtis, based on the community composition, the index of both elevation points on the subalpine zone has a same value as much as 79.8% and it can provide the approximate average value of the total of with this sort of formation, which is 409.751-553.858 tons C ha-1. Montane zone has 0.809 for the average of correlation value on the basal area parameter, 0.764 for importance value and 0.571 for density, and those values are seem lower than the ones on the subalpine zone. On the Bray-Curtis Index, it is known that approximately 58-72% of species composition is different and this formation can produce the approximate of total value of carbon stock between 362.261-506.695 tons C ha-1. Subalpine zone has a lack of average of correlation (R2) which is 0.474 on the basal area parameter, it is sufficient on the importance value as much as 0.734 and those values are lower than the ones on the subalpine and montane zone, except for density 0.586. Based on the Bray-Curtis Index, about 56-97% of species composition is different and this formation can produce the average of total value of carbon stock between 219.735-304.252 tons C ha-1. The ability of carbon stock MGPNP in million tons C is on the montane zone with maximum value of 6.463 (Br), 5.549 (Kt), 7.066 (Cv) and 5.052 (Bs), meanwhile the minimum value on the subalpine zone is 0.507 (Br), 0.483 (Kt), 0.652 (Cv) and 0.485 (Bs). Based on the conservation value aspect, special attention is needed for species that

viii includes into IUCN red list, some of them are Euonymus javanicus, Dysoxylum alliaceum, Engelhardtia spicata, Macropanax concinnus and Prunus arborea. The relationship between Shannon Index and carbon stock is modeled in cubic polynomial regression equation, because of the non-linear data spread pattern. However, this model results on the relatively small correlation value (R2 = 0.236-0.284). The results of the equation are y = 686.5x3 - 4349x2 + 8854x – 5375 (Brown), y = 542.2x3 - 3335x2 + 6543x – 3730 (Ketterings), y = 694.8x3 - 4287x2 + 8448x - 4852 (Chave) and y = 434.9x3 - 2668x2 + 5233x - 2950 (Basuki). The model using interval confidence of 95% and carbon stock value on particular Shannon Index are in the interval of lower mean and upper mean. Further research is required in order to reveal the relation between both of these variables so that the appropriate model development is obtained. The approach of ecosystem services model based on carbon stock and plant diversity can be a foundation for structuring strategy of Low Carbon Development. It is eventually aimed for the welfare of local people at Cibodas Biosphere Reserve which will give a global outcome.

ix 1.0 INTRODUCTION

One of the nature forests that become the core zone of Cibodas Biosphere Reserve is Mount Gede Pangrango National Park (MGPNP). It has been the world-admitted (UNESCO) reserve site since 1977. This site is a demonstration of sustainable development referred to a global and important agenda such as Convention on Climate Change, Convention on Biological Diversity, Agenda 21 and Global Strategy for Plant Conservation. Plant diversity on this site has a significant contribution in storing carbon stock in its biomass and in time, it should be an ecosystem service. Ecosystem services are an instrument that is capable not only to provide local advantages, but also to maintain global ecosystem. Conservation of forest biodiversity is fundamental to sustaining forests and people in a world that is adapting to climate change. Strongly focusing on forests as the key to managing the world’s carbon stocks—while disregarding the important role of biodiversity in building forest resilience (UNEP, 2011). The location of MGPNP in Sundaland is included into Global Biodiversity Hotspots (Conservation International, 2010) as the category of an area of high carbon sequestration (180-959 tons per hectare) (UNEP- WCMC, 2008). It becomes the reason that this area is one of the important, national and global agendas to conduct biodiversity conservation including plant diversity and carbon conservation. Moist tropical forests are important for carbon sequestration, because they typically have high carbon content (Table 1) and about half of the carbon in moist tropical forests contains the vegetation, a higher percentage and a much higher quantity than in any other biome (Gorte, 2009). Table 1. Average carbon stocks for various biomes (in tons per acre) Biome Plants Soil Total Tropical forests 54 55 109 Temperate forests 25 43 68 Boreal forests 29 153 182 Tundra 3 57 60 Croplands 1 36 37 Tropical savannas 13 52 65 Temp. grasslands 3 105 108 Desert/semi desert 1 19 20 Wetlands 19 287 306 Weighted Average 14 59 73 Source: Adapted from Intergovernmental Panel on Climate Change, “Table 1: Global carbon stocks invegetation and carbon pools down to a depth of 1 m [meter],” Summary for Policymakers: Land Use, Land-UseChange, and Forestry. A Special Report of the Intergovernmental Panel on Climate Change, at http://www.ipcc.ch/pub/srlulucf -e.pdf, p. 4. This biome is famous for its plant diversity, thus the carbon conservation is, by itself, a following effect of its conservation of plant diversity and its ecosystem. MGPNP as a tropical rain forest with its relatively good ecosystem condition has approximately 844 species, which spreads on the subalpine, montane and submontane zone. Varied research has been conducted on MGPNP, they were describe peak vegetation of MGPNP by C.G.C. Reinwardt (1819), noting the plants around the crater by C.L. Blume (1824), noting the plants on the west slope of Mount Pangrango by Fr. Junghuhn and G.A. Forster (1839), collections of flowering plants by S.H. Koorders (1914, 1918-1923), studying the biology of flowering plants by Docters van Leeuwen (1923, 1933), W. Meijer (1954, 1959) (Sunarno and Rugayah, 1992). One of the phenomenal studies is written by C.G.G.J. van Steenis (1972) in The Mountain Flora of Java. In 1976, Yamada conducted a research about the diversity of the species, zonal vegetation and floristic composition. It shows that MGPNP has an important role in the history of botanical research and present studies need to be conducted to complete the use of plant diversity in facing the climate change. Some of the characters of ecosystem with its high significance for global life diversity are the high number of species, playing a role as endemic species habitat and having a high social culture value (Harfst and Rein, 1988 cited in Schmidt, 2010). The effort of long-term, global plant conservation includes the management and restoration of plant diversity, the plants community and varied of connected habitat and ecosystem, in situ and ex situ. The ecosystem approach is used as a strategy for life resource management in every respect in order to obtain its sustainable benefits. The concept of sustainability is a basic relationship between culture and biosphere that is portrayed as egg-well being (Figure 1) (the Secretariat of CBD, 2002; IUCN, 2001 cited in Gibson, 2007). Therefore, the integration between conservation and development includes the aspects of ecology, economy and ethic, which complete one another. Badan Perencanaan dan Pembangunan Nasional (National Development Planning Agency of Republic of Indonesia) has placed the issue of climate change in the Mid-Long Term Plan of 2010-2014. The funds will be extended until the year of 2030 as a strategic vision in forestry, farm and other crucial sectors. The roadmap development of climate change is based on Scientific Basis Assessment, which is followed by the monitoring and evaluation efforts. Indonesia made a groundbreaking commitment to reduce emissions by 26% from business-as-usual levels by 2020 without sacrificing economic growth and to be

2

Ecosystem People & their activities

Ecosystem

Figure1. An egg of well-being a leading reservoir of carbon (Tedjakusuma, 2009; CIFOR, 2011). The efforts of mitigation become crucial as one of the solutions to reduce the cause and negative effect of climate change. It can be done by increasing the ability of the forests and vegetation in absorbing the green house gases. On the subject of climate change and REDD/REDD plus, the main problem in this research has been the unavailability of proper quantitative characteristic data, particularly the ecosystem services of carbon stock regarding its structure plant diversity. Ecosystem services assessment is applied by measuring the total of carbon stock above and below the ground, calculating the plant diversity in 3 ecological zones, analyzing the relationship of carbon stock and plant diversity and creating a simple carbon accounting. The data of carbon stock and plant diversity calculation is necessary for synthesis of ecosystem services model.

1.1 Objective of the study

The research is designed to obtain information and data of carbon stock in relation to the plant diversity on nature forest ecosystem of Mount Gede Pangrango National Park as the core zone of Cibodas Biosphere Reserve. The strong linkage between carbon stock and plant diversity will be formulated as one of the ecosystem services model on the wet climate mountains in West Java. It shall become scientific data in welcoming REDD/REDD plus and the result will directly contribute to the multistakeholder in Cibodas Biosphere Reserve for participating in reserving the main site.

3 2.0 METHODS

2.1 Study site

Mount Gede Pangrango National Park (MGPNP) is one of the tropical forest ecosystems on the mountain with wet climate and has essential scientific value, particularly on Java Island. MGPNP is geographically situated between 106º 51’ - 107º 02’ E and 6º 51’S. The park is dominated by two volcanoes: Mount Gede and Mount Pangrango. Briefly, the main ecological zone of the park can be seen in Table 2 below.

Table 2. The main ecological zone of Mount Gede Pangrango National Park Environment Vegetation Physical condition Subalpine zone  Two layers: trees & forest floor Cool and cloudy  Leaves small  Plant growth very slow Montane zone  Medium size trees all about the Cool and cloudy same height  Medium size leaves  Plant growth slow Submontane zone  Five layers of vegetation  Warm and humid including giant trees, called  Deep rich well emergent weathered soil  Species rich Source: Wiratno et al., 2004

The climate condition according to Schmidt-Ferguson is a type A (Q = 5-9%) with the range of temperature 18-25C, with the fall of rain is 3000-4000 mm per year on the average, and air humidity is 80-90%, all of which cause the formation of peaty soil (MGPNP, 2008 and 2010). The native floras of MGPNP on the subalpine zone are Vaccinium varingiaefolium, V. laurifolium, Paraserianthes lophanta, Anaphalis javanica, Leptospermum javanicum, Rhododendron retusum, Carex verticillata, Hypericum leschenaultii, Rubus lineatus, Gaultheria fragrantissima, G. leucocarpa and G. nummularioides. On the submontane until montane zone, there are Altingia excelsa, Schima wallichii, Vernonia arborea, Neolitsia cassiaefolia, Engelhardtia spicata, Flaucortia rukam, Acer laurinum, Magnolia candollei and Orophea hexandra. The kind of palm likes Javana areca-palm, rope bamboo Gigantochloa apus and forest banana Musa acuminata and Pandanus furcatus can be also found in this forest. Different kinds of species on the elevation of 2000 m asl are Lithocarpus pallidus, Castanopsis acuminatissima, Astronia spectabilis, Ardisia javanica,

4 Leptospermum flavescens, Weinmania blumei and Achronodia punctata (Sunarko and Rugayah, 1992). The width of the park as stated on Surat Keputusan Menteri Kehutanan Nomor 174/Kpts-II/2003 are 22851.03 hectare with the adjustment on the field calculation, because it has additional width from the area of Perum Perhutani which previously functioned as permanent production forest and limited production forest for 7665 hectare. This area is acknowledged by the UNESCO as core area from Cibodas Biosphere Reserve since 1977, which is coherently with buffer and transition zone. It is aimed to promote the harmony between human and biosphere with the appropriate and measured approach for sustainable development of environment. In the administrative management of West Java province, MGPNP are in 3 regencies, they are Cianjur, Bogor and Sukabumi (Soedjito, 2004; MGPNP, 2008 and 2010). This study is conducted on Mount Putri (Cianjur Regency) hiking track to Selabintana (Sukabumi Regency) by creating a sampling plot based on the elevation from 1355 to 2802 m asl as a representative of Mount Gede. Meanwhile the plot as a representative of Mount Pangrango is created on the elevation of 1079 and 1271 m asl at Cisarua Resort, Bogor Regency (Figure 2).

Figure 2. Study site map on Mount Gede Pangrango National Park (Source: USGS, 2009; MGPNP, 2010).

5 2.2 Measurement of carbon stock and estimating the biomass The research method consists of the following stages: (1) Dividing Mount Gede Pangrango National Park in its each unique ecosystem as follows: (a) submontane zone (1000-1500 m asl), (b) montane zone (1500-2400 m asl) and (c) subalpine zone (2400-3019 m asl), where, m asl is meter above sea level. (2) Measuring the carbon stock above and belowground by using World Agroforestry Center (2007) as a guide. 100 m

 40 m

 0.5m

20 m 5 m 0.5m

Figure 3. Sampling plot for carbon measurement made with length direction in line with elevation line with the assumption representing vegetation gradation on each elevation.

Diameter measurement as high as human breast (DBH) using diameter tape for big trees category is the ones that have 30 cm for diameter on the big and small plot. Meanwhile, small trees with 5-30 cm for diameter are measured on the small plot (5 x 40 m2). The biomass calculation is measured using allometric equations (Table 3).

Table 3. Allometric models used to convert measures of vegetation to AGB Aboveground component Model Source

Tree > 5 cm DBH DW = 0,118 D2,53 Brown et al. (1997) DW = 0,11 ρ D2,62 Ketterings et al. (2001) AGBest= ρ x exp(-1,499 + 2,148ln (D) + Chave et al. (2005) 0,207(ln(D))2 -0,0281(ln(D))3) Ln (TAGB) = c + αln(DBH) Basuki et al. (2009) Palms > 5 cm DBH DW = 4.5 + 7.7H Frangi and Lugo (1985) Ferns > 5 cm DBH DW = ρ H D2/40 Hairiah et al. (1999)

Note: AGB = aboveground biomass (kg), TAGB = total aboveground biomass (kg), DW = dry weight (kg), D = diameter (cm), DBH = diameter at breast height (cm), H = tree height (cm), c = intercept, α = slope coefficient of regression equation, ρ = wood mass (cm3).

6 Carbon concentration (C) in organic ingredients is usually 46%, thus carbon stock can be calculated by multiplying the total of its mass weight with 0.46 (Hairiah and Rahayu, 2007). The writing of allometric equation names in this report uses the main writer’s names as representation and simplicity, they are Br = Brown et al., Kt = Ketterings et al., Cv = Chave et al. and Bs = Basuki et al. Wood density is based on the database in http://www.worldagroforestrycenter.org and global wood density database in http://hdl.handle.net/10255/dryad.235. The measurement for understorey biomass is conducted destructively placing a quadrant size 0.5 x 0.5 m2 on the 6 crossing points in a plot of 5 x 40 m2. This small quadrant is also to take biomass of coarse litter, fine litter, fine root and sample of composite soil. Fine litter is sieved on the 2 mm pore holes. Fresh weight per biomass part is weighed and then 100-300 grams are taken for sub-sample, except fine litter for 100 grams, then it is dried on the temperature of 80C for 2 x 24 hours. Calculating total dry weight of understorey, coarse litter, fine litter and fine root per quadrant uses the following formula: Total DW = (DW (gr)/FW subsample (gr)) x Total FW Where, DW is dry weight, FW is fresh weight The measurement of wood necromass is to standing or fallen dead trees, intact plant stumps, branches and twigs with the diameter of 5 cm and 50 cm long. The method of measurement is by measuring the diameter and length (height). Should there be a stem lie across, then the stem diameter is measured on 3 positions (top, middle, down). The calculation of wood density is conducted by taking the wood sample minimum at size 3x 3 x 3 cm3 for 3 times of repetitions, then weighing the fresh weight, after that putting into an oven for a temperature of 80C for 2 x 24 hours. Carbon stock below ground is taken from the composite soil sample on the quadrant 0.5 x 0.5 m2 with 6 points and 0-10 cm deep and 10-20 cm deep for pH analysis and C organic percentage at Indonesian Soil Research Institute, Ministry of Agriculture, Republic of Indonesia. The soil sample taking is not obstructed for soil texture in the laboratory; meanwhile for soil bulk density measurement uses the ring sample carefully. The soil sample is weighed for its fresh weight then dried in an oven with temperature of 105C for 2 x 24 hours and weighed for its dry weight (W2). The calculation of bulk density is with the following formula: BD = W2 (g)/V (soil volume in cm3)

7 The value of soil carbon can be predicted by using the following equation: SC = %C-org x BD x AD Where, SC is soil carbon (tons), BD is bulk density (tons/m3), D is soil sample depth (m), A is width of sample soil area (m2), %C org is % organic carbon

Total carbon per width unit is a totaling of trees biomass carbon, understorey biomass carbon, necromass and soil carbon.

2.3 Plant diversity

The plant diversity calculation uses ecological index that includes the following parameter (Mueller-Dombois and Ellenberg, 1974 cited in Partomihardjo and Rahajoe, 2004; Stiling, 1996; Pontasch et al., 1989; Michie, 1982). (1) Basal area: r2 (m2 ha-1) Relative Basal area (%): (Species basal area/total basal area) x 100% Where, r is radius and diameter measurement of breast height

(2) Density: total number of individuals of species/sample plot width Relative Density: (total number of individuals of species/total all individuals) x 100%

(3) Relative Frequency: (total plot of the found species/total all studied plots) x 100%

(4) Importance value: Relative Basal Area + Relative Density + Relative Frequency Importance value per sample plot: Relative Basal Area + Relative Density

(5) Shannon-Wiener diversity index:

H’ = piln (pi)

Where, H’ is Shannon-Wiener diversity index, pi is the proportion of individuals belonging to species I and ln is natural log (i.e., base 2.718).

(5) Bray-Curtis Index:

1- BCij =

8 Where, 1-BCij is the similarity between two site/sample plot, i and j each defined by a set of n attributes, Xik, and Xjk is number of individuals in species.

Species name uses Tropicos® website as the reference (http://www.tropicos.org/), IPNI (The International Plant Names Index) (http://www.ipni.org) and Kewensis Index 2.0 from Oxford University Press (1997). The checking about conservation status of plants species found on the observation plot is based on the IUCN criteria IUCN (http://www.iucnredlist.org).

2.4 Relationship between carbon stock and plant diversity

The relationship between carbon stock and plant diversity is calculated by using regression and correlation equation with Excel 2008 installed in Mac OS X Version 10.5.8 system. The relationship modeling between Shannon Index to carbon stock uses polynomial order 3 regression (cubic) by putting in prediction value and model test with value of LMCI (lower mean confidence interval) and UMCI (upper mean confidence interval).

2.5 Carbon value

Estimation of carbon stock is calculated on its 3 ecological zones, they are subalpine zone, montane zone and submontane zone. The carbon stock average obtained from 4 allometric equations is each multiplied by the zone width, therefore, the information about the ability of supporting carbon from Mount GedePangrango National Park is achieved in unit of millions of tons C.

2.6 Ecosystem service The structuring of ecosystem services model based on carbon stock and plant diversity is synthesized based on statistic calculation data, which is obtained and referred to the theory and consideration of applied management for carbon stock case in climate change context.

9 3.0 RESULTS AND DISCUSSION

3.1 Estimation carbon stock and biomass

The average result of carbon stock measurement (aboveground carbon stock) on 9 sites using four allometric equations in tons C per hectare is 399.717 (Brown/Br), 354.648 (Ketterings/Kt), 449.688 (Chave/Cv) and 325.724 (Basuki/Bs) (Figure 4).This average value of the carbon stock is varied depending on the elevation between 233.837-597.554 (Br), 172.368-503.435 (Kt), 192.372-649.759 (Cv) and 156.82-454.645 (Bs).

Brown Biomassa Brown Carbon stock 1600 Ketterings Biomassa Ketterings Carbon stock

) Chave Biomassa Chave Carbon stock -1 1400 Basuki Biomassa Basuki Carbon stock 1200

1000 800 600

rbon &AGB (tons C ha C (tons &AGB rbon 400 Ca 200 0

2802 2601 2329 2075 1851 1710 1355 1271 1079 Elevation (m asl) Figure 4. Estimation of carbon stock and biomass on several elevations, AGB is aboveground biomass that consist of trees, understorey, litter and necromass.

The estimated biomass is assumed that its 46% is stored carbon stock on certain elevation. The average value of the biomass is as much as 868.950 (Br), 770.974 (Kt), 977.583 (Cv) and 708.095 (Bs), with variations between 508.341-1299.031 (Br), 374.714-1094.424 (Kt), 418.2-1412.52 (Cv) and 340.912-988.355 (Bs). The highest carbon stock is found on 2329 m asl (montane zone), except Bs allometric on 2601 m asl (subalpine zone), and the lowest one is found on 1355 m asl (Br) and 1079 m asl (Kt, Cv, Bs). The average of highest carbon stock based on zone is on the subalpine zone, that is 409.751 (Kt), 553.858 (Cv), 411.661(Bs) except Br (463.466) on the montane zone. It is presumably caused by contribution of Vaccinium varingiaefolium, which is multistem as well as dominant species on the subalpine zone. One of the abundant species in a dense forest, precisely at fenced area of Mount Gede crater, is Vaccinium varingiaefolium (Sunarno and Rugayah, 1992). The contribution of carbon stock from Vaccinium varingiaefolium on the elevation of 2802 m asl

10 is the biggest one of all other species, that is 173.632 (Br), 165.136 (Kt), 237.518 (Cv) and 158.797 (Bs), meanwhile on 2601 is 206.293 (Br), 196.801 (Kt), 276.57 (Cv) and 187.467 (Bs). On the other side, when thoroughly observing the total of area per ecological zone, then the biggest carbon stock is on the montane zone, because it is the widest zone (61% total width) compared to two other zones (Table 4).

Table 4. The average carbon stock in observation plot at 3 zones of MGPNP Zone

Allometric Subalpine Montane Submontane Brown 430.701 463.466 294.082 Ketterings et al. 409.751 397.980 260.157 Chave et al. 553.858 506.695 304.252

Basuki et al. 411.661 362.261 219.735 Note: Carbon stock is a mix aboveground and soil carbon in tons C ha-1.

According to Figure 5, it can be seen that the maximum proximity value from the big trees to the small ones happens on the subalpine zone, that is 280 individuals ha-1 of the big tree (2601 m asl) and 2950 individuals ha-1 (2802 m asl). On the other side, the minimum proximity is shown on the submontane zone, which is 55 individuals ha-1 big trees (1079 m asl) and 250 individuals ha-1 small trees (1271 m asl).

3500 )

-1 3000

2500

2000

1500

ha (individualsnsity 1000 De 500

0 Elevation (m asl)

Small tree Big tree

Figure 5. The value of trees proximity based on the elevation zone. The maximum value of big trees are on the elevation of 2601 m asl, meanwhile the small trees is found on the elevation of 2802 m asl.

11 Brown Ketterings

600 500

450 500 400 ) ) -1 -1 350 400 300

300 250

200 200 150 rbon rbon stock (tons C ha arbon stock arbon (tons C ha c Ca 100 100 50 0 0 02 01 29 75 51 10 55 71 79 28 26 23 20 18 17 13 12 10 2802 2601 2329 2075 1851 1710 1355 1271 1079 Elevation (m asl) Elevation (m asl) Big tree Small tree Big tree Small tree

Chave Basuki

700 450

600 400 ) -1 ) 350 -1 500 300 400 250

200 300 150

200 rbon stock (tons C ha rbon rbon stock (tons C ha Ca 100 Ca 100 50

0 0 02 01 29 75 51 10 55 71 79 02 01 29 51 10 55 71 79

28 26 23 20 18 17 13 12 10 28 26 23 2075 18 17 13 12 10 Elevation (m asl) Elevation (m asl) Big tree Small tree Big tree Small tree

Figure 6. Carbon stock is stored in two classes of tree sizes on different elevations. The maximum value of big trees are equal for all allometic equations, that is on the elevation of 1271 m asl, meanwhile small trees are on 2329 m asl. The minimum value of big trees is on the elevation of 1355 m asl, and for small trees are on the elevation of 1271 m asl.

12 The measurement of carbon stock in trees biomass is conducted in two classes of tree size; those are big trees (DBH >30 cm) and small trees (DBH 5-30 cm). The approximate values of carbon stock (tons C ha-1) on big trees are 65.968-338.997 (Br), 56.853-346.307 (Kt), 81.351-390.052 (Cv) and 51.512-218.377 (Bs); and on the small trees are 17.208- 314.374 (Br), 13.93-258.749 (Kt), 19.621-341.978 (Cv) and 16.837-227.082 (Bs). Meanwhile the approximate average of contribution on the aboveground carbon stock from the big trees 42.697-51.963% and from the small trees 29.664-35.892%. The contrast combination happens on the elevation of 1271 m asl, which the maximum values of carbon stock from the big trees are along with small trees (Figure 6). It is a result of the maximum proximity of big trees per hectare and the minimum proximity of small trees. The tree component has the significant average contribution value to the total carbon stock, that is 82.803% (Br), 80.692% (Kt), 84.994% (Cv) and 79.766% (Bs) (Figure 7). It shows the role of tree biomass is highly essential in supporting carbon. On the other side, the average contribution on the aboveground carbon stock from under storey is as much as 1.302% (Br), 1.517% (Kt), 1.198% (Cv) and 1.559% (Bs); from litter is 12.895% (Br), 14.83% (Kt), 11.778% (Cv) and 15.735% (Bs); and from necromass is 3.308% (Br), 3.932% (Kt), 3.156% (Cv) and 4.117% (Bs). Contribution from litter is relatively higher than necromass and understorey and based on Clark's research (2001), it shows that fine litterfall has high correlation (R2 = 0.69) towards the aboveground biomass increment in tropical forest. Use of allometric equation in calculation influences the difference of tree contribution percentage to the total aboveground biomass and carbon stock (Table 5).

Table 5. The average contribution of carbon stock from trees on 4 allometric equations Elevation Allometric equations (%) Brown Ketterings Chave Basuki (m asl) 2802 78.905 77.249 83.017 78.226 2601 87.308 86.922 90.390 86.577 2329 88.263 86.069 89.206 84.035 2075 85.191 81.809 85.514 79.892 1851 73.952 70.830 77.567 71.106 1710 86.622 84.997 88.018 83.302 1355 74.514 69.865 77.132 72.545 1271 87.673 87.794 89.107 82.445 1079 80.030 71.951 74.868 69.170 Average 82.803 80.692 84.994 79.766

13 Brown Ketterings

Tree 600 Tree 500 Understorey Understorey 450 Litter Necromass

) 500 Litter -1 ) 400 Necromass -1 350 400 300 300 250

200 200 rbon rbon stock (tons C ha 150 rbon rbon stock (tons C ha Ca Ca 100 100 50 0 0 02 01 29 75 51 10 55 71 79 02 01 29 75 51 10 55 71 79 28 26 23 20 18 17 13 12 10 28 26 23 20 18 17 13 12 10 Elevation (m asl) Elevation (m asl)

Chave Basuki

700 Tree 450 Tree Understorey Understorey Litter 400 Litter 600 Necromass Necromass 350 )

) -1 -1 500 300

400 250 200 300

150 rbon rbon stock (tons C ha rbon stock (tons C ha 200 Ca Ca 100 100 50

0 0 5 02 01 29 51 10 55 71 79

02 01 29 75 51 10 55 71 79 28 26 23 207 18 17 13 12 10 28 26 23 20 18 17 13 12 10 Elevation (m asl) Elevation (m asl)

Figure 7. Contribution of tree component, understorey, litter and necromass for carbon stock on different elevations.

14 3.2 Plant diversity as the carbon stock performance and its conservation value

This research shows that approximately 66 native species of Mount Gede Pangrango National Park, most of which are in the family of Lauraceae, Fagaceae, Myrtaceae and Moraceae. Some of the species becomes dominance on certain elevations, such as Leptospermum flavescens, Lithocarpus sundaicus, Neolitsea javanica, Castanopsis javanica, Castanopsis tungurrut, Castanopsis acuminatissima and Phoebe grandis. On the elevation of 2601 m asl and 1710 m asl, there is Paraserianthes lophantha (kemlandingan gunung) which is called mountain mass elevation by Steenis (1972), that is the elevation approximation found between 1100-3100 m asl. Meanwhile, the height of lowest peak where species are found is on the 2500 m asl, and the effect is on the elevation of 1400 m asl. The most number of species are found at Selabintana V (1710 m asl), there are 25 species, followed by Pasir Ipis (1355 m asl) with 23 species, thus both of them have relatively higher Shannon Index than other elevations, and those are 2.925 and 2.701. The least number of species from observation plot is on the elevation of 2802 m asl with 7 species. The interesting fact is that Schima wallichii is found from the elevation of 1271 (submontane) until 2601 m asl (subalpine) and it is shown with a high frequency of 0.778 and importance value of 36.865 (Table 6). This finding eventually makes Schima wallichii has the highest carbon stock value (451.682/Bs-652.434/Br), particularly if it is compared with the minimum value of carbon stock from Ziziphus angustifolia (0.232/Br) and Laportea stimulans (0.103/Kt-0.242/Bs) (Figure 22 and Appendix 1). In the meantime, the special species at the crater, Vaccinium varingiaefolium, has the highest importance value of 49.152, and though with only small frequency of 0.222, with basal area is 314.714 m2 ha-1 widths. On the other side, it is important to know that the plot on this crater (2802 m asl) has the lowest Shannon Index that is 1.409. Based on the Table 6 and Figure 8, the population of Vaccinium varingiaefolium is so many that they have the high value of basal area, followed by Schima wallichii, Myrsine hasseltii and Symplocos odoratissima. Other species that has relatively high frequency (0.556) are Lithocarpus sundaicus (subalpine-submontane) and Cyathea latebrosa (montane-submontane). Even though Castanopsis acuminatissima has only low frequency (0.111), it has a high basal area and high importance value. It is a result of the relatively big accumulation value (11.86 m) of tree diameter. Some species has relatively small frequency value (0.111-0.333), however, they are with high importance value on some elevations such as Phoebe grandis, Oriocnide scabra and Platea latifolia.

15 Table 6. Ecological index of 30 species with highest importance value on study site

Species Basal area Density Frequency Importance (m2 ha-1) (individuals ha-1) Value Vaccinium varingiaefolium 314.714 405 0.222 49.152 Schima wallichii 174.821 390 0.778 36.865 Myrsine hasseltii 38.072 285 0.444 16.032 Leptospermum flavescens 91.457 135 0.222 15.903 Lithocarpus sundaicus 52.790 125 0.556 13.573 Catanopsis acuminatissima 110.423 20 0.111 13.295 Symplocos odoratissima 18.452 245 0.333 11.845 Platea latifolia 19.634 115 0.333 8.014 Neolitsea javanica 11.770 105 0.444 7.597 Castanopsis tungurrut 7.529 135 0.222 6.498 Castanopsis javanica 7.820 105 0.333 6.385 Cyathea latebrosa 0.096 55 0.556 5.534 Neolitsea cassiaefolia 1.499 60 0.444 5.074 Elaeocarpus angustifolius 3.321 75 0.333 4.966 Polyosma integrifolia 1.193 55 0.444 4.887 Syzygium rostratum 2.443 60 0.333 4.411 Elaeocarpus stipularis 2.233 60 0.333 4.387 Macropanax dispermum 0.744 60 0.333 4.220 Ardisia javanica 0.272 35 0.444 4.175 Oreocnide scabra 2.999 75 0.222 4.161 Vernonia arborea 7.744 55 0.222 4.083 Euonymus javanicus 0.176 55 0.333 4.004 Macropanax concinnus 0.321 45 0.333 3.716 Lithocarpus pallidus 0.858 20 0.333 3.014 Phoebe grandis 4.416 55 0.111 2.941 Acronychia pedunculata 1.466 15 0.333 2.929 Dysoxylum alliaceum 1.290 35 0.222 2.750 Trema orientalis 3.717 25 0.222 2.717 Astronia spectabilis 1.264 30 0.222 2.595 Flacourtia rukam 0.005 45 0.111 2.142

16 Ziziphus angustifolia Weinmannia blumei Vernonia arborea Vaccinium… Urophyllum arboreum Turpinia sphaerocarpa Trema orientalis Toona sureni Syzygium rostratum Syzygium racemosum Syzygium antisepticum Symplocos odoratissima Schima wallichii Schefflera scandens Rapanea hasseltii Prunus arborea Polyosma integrifolia Platea latifolia Pinanga coronata Phoebe grandis Paraserianthes lophanta Oreocnide scabra Neolitsea javanica Neolitsea cassiaefolia Myrsine hasseltii Magnolia lilifera Macropanax dispermum Macropanax cocinnum Litsea mappacea Lithocarpus sundaica Lithocarpus pallida Lindera polyantha Leptospermum … Leptospermum … Laportea stimulans Lagerstroemia speciosa Lagerstroemia indica Itea macrophylla Gordonia excelsa Flacourtia rukam Ficus ribes Ficus padana Ficus heterophylla Ficus fistulosa Ficus cuspidata Euonymus javanicus Engelhardtia spicata Elaeocarpus stipularis Elaeocarpus … Dysoxylum alliaceum Dacrycarpus imbricatus Cyathea spinulosa Cyathea latebrosa Cyathea junghuhniana Cryptocarya ferrea Catanopsis… Castanopsis tungurrut Castanopsis javanica Astronia spectabilis Ardisia javanica

Antidesma tetrandum Alangium chinense Aglaia eclliptica Acronychia trifoliolata Acronychia pedunculata Acer laurinum

0 20 40 60 80 100

Number of individuals Figure 8. Composition and abundance of plant species on 3 ecology zones MGPNP.

17 Based on tree proximity class per hectare, it shows that its 68.182% is classified into class occasionally (0-50), and its 25.76% is classified into class often (51-150) and the rest is class abundant (100-450) that has relatively small proportion, that is 6.06%.

Table 7. Class and category of tree density Class of density Proportion Category (individuals ha-1) (%) 0-50 68.182 occasionally 51-100 16.667 often 101-150 9.091 often 151-200 0.000 abundant 201-250 1.515 abundant 251-300 1.515 abundant 301-350 0.000 abundant 351-400 1.515 abundant 401-450 1.515 abundant

450 420 390 ) -1 360 330 300 270 240 210 180 150

ndance (individuals ha 120 90 Abu 60 30 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 Species density rank

Figure 9. The relationship between species density rank and its abundance. Species with high abundance are Vaccinium varingiaefolium, Schima wallichii, Myrsine hasseltii, Leptospermum flavescens, Lithocarpus sundaicus, Catanopsis acuminatissima, Symplocos odoratissima, Platea latifolia, Neolitsea javanica and Castanopsis tungurrut. Data can be referred to Appendix 4.

18 On subalpine zone, there are dominant species on wet-climate mountains such as Vaccinium varingiaefolium, Symplocos odoratissima, Myrsine hasseltii and Leptospermum flavescens and each has the importance value of 211.155, 51.594, 66.085 and 60.275 (Table 8 and Figure 10).

Vaccinium varingiaefolium Symplocos odoratissima Schefflera scandens

Schima wallichii Polyosma integrifolia Paraserianthes lophanta Myrsine hasseltii Macropanax concinnus Leptospermum javanicum Leptospermum flavescens Cyathea latebrosa Ardisia javanica 0 10 20 30 40 50 60 70 80 90

Number of individuals Figure 10. Composition and abundance of plant species on subalpine zone.

Montane zone is the widest zone of the other two zones, and species with highest abundance is found in the zone, those are Schima wallichii (81.115), Lithocarpus sundaicus (291.32), Platea latifolia (49.593), Neolitsea javanica (33.273), and Elaeocarpus angustifolius (24.957) (Table 8 and Figure 11). Some species found on the montane and subalpine zone are included into IUCN red list, therefore it is important to protect them from any disturbance and the threatened species populations will be sustained for a long term by protecting the types of habitat (Widyatmoko, 2010). Some species can found here, Euonymus javanicus (Celastraceae), Dysoxylum alliaceum (Meliaceae), Engelhardtia spicata (Juglandaceae), the ones with the status of Lower Risk (LR)/least concern, that is does not qualify for a more at risk category, widespread and abundant taxa, are included in this category. Furthermore, Macropanax concinnus (Araliaceae) is in category Vulnerable B1+2c, it means it is facing a high risk of extinction in the wild in the medium-term future, severely fragmented or known to exist at no more than ten locations, also continuing decline, inferred, observed or projected because of area, extent and/or quality of habitats. The frequency of Euonymus javanicus is 0.333 (elevation 1851, 1710, 1355) with density of 55

19 trees per hectare, meanwhile, the frequency of Dysoxylum alliaceum is 0.222 (elevation 1851, 1710) with density of 35, the frequency of Engelhardtia spicata is 0.111 (elevation 2329) with relatively low density of 5 trees per hectare. Macropanax concinnus that has frequency of 0.333 (elevation 2802, 1851, 1710) and density of 45 trees per hectare requires main attention for conservation.

Weinmannia blumei Vernonia arborea Urophyllum arboreum Turpinia sphaerocarpa Syzygium rostratum Syzygium racemosum Syzygium antisepticum Schima wallichii Polyosma integrifolia Platea latifolia Paraserianthes lophanta Oreocnide scabra Neolitsea javanica Neolitsea cassiaefolia Myrsine hasseltii Magnolia lilifera Macropanax dispermum Macropanax concinnus Litsea mappacea Lithocarpus sundaicus Lithocarpus pallidus Lindera polyantha Lagerstroemia speciosa Flacourtia rukam Ficus cuspidata Euonymus javanicus Engelhardtia spicata Elaeocarpus stipularis Elaeocarpus angustifolius Dysoxylum alliaceum Dacrycarpus imbricatus Cyathea latebrosa Cyathea junghuhniana Cryptocarya ferrea Castanopsis tungurrut Castanopsis javanica Astronia spectabilis Ardisia javanica Antidesma tetrandum Acronychia pedunculata Acer laurinum 0 10 20 30 40 50 60 70 Number of individuals

Figure 11. Composition and abundance of plant species on montane zone MGPNP.

20 The most abundance on submontane zone with each highest importance value are Schima wallichii (83.585), Castanopsis tungurrut (57.179), Oriocnide scabra (78.753), Castanopsis javanica (35.036) and Phoebe grandis (42.515) (Table 8 and Figure 12). Prunus arborea (Rosaceae), which includes the category of LR/lower risk, with frequency of 0.111 and estimated density of 5 trees per hectare, is only found on the elevation of 1355 m asl.

Ziziphus angustifolia Vernonia arborea Urophyllum arboreum Trema orientalis Toona sureni Symplocos odoratissima Schima wallichii Rapanea hasseltii Prunus arborea Pinanga coronata Phoebe grandis Oreocnide scabra Neolitsea javanica Neolitsea cassiaefolia Macropanax dispermum Lithocarpus sundaica Lithocarpus pallida Lindera polyantha Laportea stimulans Lagerstroemia indica Itea macrophylla Gordonia excelsa Ficus ribes Ficus padana Ficus heterophylla Ficus fistulosa Euonymus javanicus Elaeocarpus stipularis Elaeocarpus angustifolius Dysoxylum alliaceum Cyathea spinulosa Cyathea latebrosa Catanopsis acuminatissima Castanopsis tungurrut Castanopsis javanica Antidesma tetrandum Alangium chinense Aglaia eclliptica Acronychia pedunculata Acronychia trifoliolata

0 5 10 15 20 25

Number of individuals

Figure 12. Composition and abundance of plant species on submontane zone of MGPNP.

The stratification of Java Mountains forests based on its configuration trees can be observed on site (Steenis, 1972). Based on the importance value on Table 8, the first tree layer/canopy is Schima wallichii, Vernonia arborea, Engelhardtia spicata, Elaeocarpus

21 angustofolius and Elaeocarpus stipularis, the second layer is Symplocos odoratissima, Acronychia pedunculata, while Ardisia javanica is small tree at the third layer.

Table 8. Relative value of ecological index component on Mount Gede Pangrango National Park Elevation Species Relative Relative Importance (m asl)/Zone Basal area Density Value 2802 Myrsine haseltii 9.696 26.606 36.301 Subalpine Symplocos odoratissima 6.191 26.606 32.797 Vaccinium varingiifolium 83.600 37.615 121.215 Other species (4) 0.513 9.174 9.687 2601 Leptospermum flavescens 35.997 20.175 56.172 Subalpine Myrsine hasseltii 6.976 22.807 29.784 Vaccinium varingiifolium 54.853 35.088 89.940 Symplocos odoratissima 2.131 16.667 18.798 Ardisia javanica 0.001 0.877 0.878 Schima wallichii 0.021 1.754 1.776 Other species (3) 0.021 2.632 2.652 2329 Lithocarpus sundaicus 37.641 21.429 59.069 Montana Schima wallichii 53.203 28.571 81.774 Vernonia arborea 6.988 14.286 21.274 Ardisia javanica 0.004 1.429 1.432 Engelhardtia spicata 0.008 1.429 1.436 Other species (7) 2.158 32.857 35.015 2075 Neolitsea javanica 14.963 18.310 33.273 Montana Platea latifolia 27.058 22.535 49.593 Schima wallichii 47.637 16.901 64.538 Elaeocarpus stipularis 0.301 1.408 1.709 Others species (12) 10.042 40.845 50.587 1851 Castanopsis javanica 10.593 10.000 20.593 Montana Castanopsis tungurut 22.369 10.000 32.369 Schima wallichii 36.220 17.143 53.363 Ardisia javanica 0.754 4.286 5.04 Elaeocarpus angustifolius 8.892 10 18.892 Elaeocarpus stipularis 4.617 8.571 13.189 Others species (11) 16.554 40 56.554 1710 Schima wallichii 77.504 14.141 91.645 Montana Lithocarpus sundaicus 14.975 7.071 22.046 Acronychia pedunculata 0.135 1.01 1.145 Ardisia javanica 0.019 2.02 2.039 Elaeocarpus angustifolius 0.005 6.061 6.065 Others species (20) 7.521 78.788 86.309 1355 Castanopsis javanica 19.543 15.493 35.036 Submontane Phoebe grandis 27.022 15.493 42.515 Schima wallichii 41.025 15.493 56.518 Acronychia pedunculata 1.045 7.042 8.087 Elaeocarpus stipularis 0.477 4.225 4.702 Other species (19) 10.888 42.254 53.142 1271 Castanopsis tungurut 0.036 57.143 57.179 Submontane Catanopsis acuminatissima 92.349 2.857 95.206 Schima wallichii 7.067 20.000 27.067 Acronychia trifoliolata 0.063 2.857 2.921 Elaeocarpus angustifolius 0.286 5.714 6 Other species (4) 0.198 11.429 11.626 1079 Oriochnide scabra 32.087 46.667 78.753 Submontane Toona sureni 19.805 3.333 23.138 Trema orientalis 38.680 13.333 52.014 Vernonia arborea 1.786 3.333 5.119 Other species (8) 7.642 33.333 40.975

22 The following description shows equal regressions and correlation values out of the efforts to identify the relationship between carbon stock and plant diversity and they can be observed on each elevation. (A) Gunung Gemuruh

300 250 ) 250 ) -1

-1 200

200 150

k (tons C ha k 150 k (tons C ha k 100 100

Carbon stoc 50 50 Carbon stoc

0 0 0 100 200 300 400 500 600 700 800 900 1000 0 20 40 60 80 100 120 140 160 180 200 220

Basal area (m2 ha-1) Density (individuals ha-1)

Brown Ketterings Brown Ketterings Chave Basuki Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Brown) Linear (Ketterings ) Linear (Chave) Linear (Basuki) Linear (Chave ) Linear (Basuki )

Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = 0.181x + 15.93 R² = 0.962 Brown y = 0.613x - 4.408 R² = 0.732 Ketterings y= 0.174x + 13.09 R² = 0.980 Ketterings y = 0.563x - 4.474 R² = 0.683 Chave y = 0.255x + 14.86 R² = 0.976 Chave y = 0.841x - 12.01 R² = 0.704 Basuki y= 0.167x + 14.49 R² = 0.938 Basuki y = 0.594x - 6.526 R² = 0.789

13. The relationship of basal area, density 250 Fig. and importance value to carbon stock on the )

-1 200 elevation of 2802 m asl (plot Gunung Gemuruh). Basal area and importance value 150 parameter show a high correlation value, which (tons C ha means both parameters influence carbon stock 100 for 93.8-98% and 99-99.7%. Density influences the carbon stock for 68.3-78.9%. The increase 50 Carbon stock of carbon stock as the result of adding one value on each parameter is 0.167-0.255 units 0 from basal area, 0.563-841 units from density 0 25 50 75 100 125 150 Importance value and 1.228-1.923 unit from importance value.

Brown Ketterings Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Allometric Equations Determinant coefficients Brown y = 1.375x + 4.022 R² = 0.992 Ketterings y = 1.288x + 2.936 R² = 0.997 Chave y = 1.923x - 1.481 R² = 0.990 Basuki y = 1.288x + 2.936 R² = 0.997

23 (B) Selabintana I

300 300

250 250 ) ) -1 -1 200 200

k (tons C ha k k (tons C ha k 150 150

100 100 Carbon stoc Carbon stoc 50 50

0 0 0 100 200 300 400 500 600 700 800 0 20 40 60 80 100 120 140 160 180 200 220 Basal area (m2 ha-1) Density (individuals ha-1)

Brown Ketterings Brown Ketterings Chave Basuki Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Linear (Chave) Linear (Basuki) Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = 0.276x + 7.613 R² = 0.973 Brown y = 0.872x - 9.009 R² = 0.816 Ketterings y = 0.278x + 5.858 R² = 0.990 Ketterings y = 0.851x - 9.155 R² = 0.779 Chave y = 0.389x + 8.890 R² = 0.988 Chave y = 1.2x - 12.64 R² = 0.788 Basuki y = 0.249x + 8.633 R² = 0.959 Basuki y = 0.808x - 7.782 R² = 0.851

300 Fig. 14. The relationship of basal area, density and importance value to carbon stock on the 250 )

-1 elevation of 2601 m asl (plot Selabintana I).

200 Basal area and importance value parameter show a high correlation value, which means

k (tons C ha k 150 both parameters influence carbon stock 95.9-99% and 98.2-99.4%. Density influences 100 the carbon stock for 77.9-85.1%. The increase

Carbon stoc of carbon stock as the result of adding one 50 value on each parameter is 0.249-0.389 units 0 from basal area, 0.808-1.2 units from density 0 10 20 30 40 50 60 70 80 90 100 and 2.026-3.109 units from importance value. Importance value Brown Ketterings Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Allometric Equations Determinant coefficients Brown y = 2.226x - 3.212 R² = 0.988 Ketterings y = 2.216x - 4.472 R² = 0.982 Chave y = 3.109x - 5.740 R² = 0.985 Basuki y = 2.026x - 1.593 R² = 0.994

24 (C) Selabintana II

350 350 ) -1

300 ) 300 -1 250 250 200 k (tons C ha k 200

150 (tons C ha k 150 100 100 Carbon stoc 50

Carbon stoc 50 0 0 0 50 100 150 200 250 300 350 Basal area (m2 ha-1) 0 25 50 75 100 125 Density (individuals ha-1) Brown Ketterings Chave Basuki Brown Ketterings Linear (Brown) Linear (Ketterings) Chave Basuki Linear (Chave) Linear (Basuki) Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = 0.841x + 4.178 R² = 0.984 Brown y = 2.306x - 25.08 R² = 0.805 Ketterings y = 0.727x + 2.365 R² = 0.961 Ketterings y = 1.971x - 22.29 R² = 0.770 Chave y = 0.971x + 3.093 R² = 0.972 Chave y = 2.645x - 30.17 R² = 0.785 Basuki y = 0.552x + 4.726 R² = 0.973 Basuki y = 1.525x - 14.80 R² = 0.808

Fig. 15. The relationship of basal area, density 350 and importance value to carbon stock on the 300 elevation of 2329 m asl (plot Selabintana II). ) -1 250 Basal area and importance value parameter show a high correlation value, which means 200

k (tons ha k both parameters influence carbon stock 150 96.1-98.4%. Density influences the carbon

100 stock for 77-80.8%. The increase of carbon

Carbon stoc stock as the result of adding one value on each 50 parameter is 0.552-0.971 units from basal area, 0 1.525-2.645 units from density and 2.003-3.508 0 15 30 45 60 75 90 units from importance value. Importance value . Brown Ketterings Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Allometric Equations Determinant coefficients Brown y = 3.045x - 8.551 R² = 0.960 Ketterings y = 2.622x - 8.485 R² = 0.932 Chave y = 3.508x - 11.48 R² = 0.945 Basuki y = 2.003x - 3.707 R² = 0.954

25 (D) Selabintana III

120 120

) 100 )

-1 100 -1 80 80

k (tons C ha k 60

k (tons C ha k 60

40 40

Carbon stoc 20 Carbon stoc 20

0 0 0 20 40 60 80 100 120 140 160 180 0 10 20 30 40 50 60 70 80 90 Basal area (m2 ha-1) Density (individuals ha-1) Brown Ketterings Brown Ketterings Chave Basuki Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Linear (Chave) Linear (Basuki) Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = 0.540x + 11.09 R² = 0.677 Brown y = 0.812x + 4.530 R² = 0.457 Ketterings y = 0.415x + 9.058 R² = 0.682 Ketterings y = 0.542x + 5.815 R² = 0.348 Chave y = 0.586x + 11.04 R² = 0.786 Chave y = 0.814x + 5.413 R² = 0.452 Basuki y = 0.364x + 7.963 R² = 0.783 Basuki y = 0.570x + 3.054 R² = 0.570

Fig. 16. The relationship of basal area, density 120 and importance value to carbon stock on the elevation of 2075 m asl (plot Selabintana III). 100 )

-1 Basal area and importance value parameter 80 show a relatively similar correlation value, which means each parameter influences carbon

k (tons C ha k 60 stock for 67.7-78.6% and 60.7-77.1%. Density influences the carbon stock for 34.8-57%. The 40 increase of carbon stock as the result of adding

Carbon stoc 20 one value on each parameter is 0.364-58.6 units from basal area, 0.542-0.814 units from density 0 and 0.844-1.31 units from importance value. 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 Importance value

Brown Ketterings Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Allometric Equations Determinant coefficients Brown y = 1.235x + 7.116 R² = 0.652 Ketterings y = 0.911x + 6.463 R² = 0.607 Chave y = 1.310x + 7.106 R² = 0.722 Basuki y = 0.844x + 5.152 R² = 0.771

26 (E) Selabintana IV

80 70

70 60 ) ) -1 -1 60 50 50 40 k (tons C ha k

40 (tons C ha k 30 30

20 20 Carbon stoc Carbon stoc 10 10 0 0 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70

2 -1 -1 Basal area (m ha ) Density (individuals ha ) Brown Ketterings Brown Ketterings Chave Basuki Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Linear (Chave) Linear (Basuki) Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = 0.848x + 4.674 R² = 0.892 Brown y = 0.839x - 4.271 R² = 0.880 Ketterings y = 0.772x + 3.530 R² = 0.860 Ketterings y = 0.763x - 4.576 R² = 0.843 Chave y = 1.055x + 5.474 R² = 0.831 Chave y = 1.063x - 6.043 R² = 0.850 Basuki y = 0.649x + 4.899 R² = 0.728 Basuki y = 0.707x - 3.276 R² = 0.870

80 Fig. 17. The relationship of basal area, density and importance value to carbon stock on the 70 elevation of 1851 m asl (plot Selabintana IV). )

-1 60 Basal area, density and importance value parameter show a relatively similar correlation 50 value, which means each parameter influences

k (tons C ha k 40 carbon stock for 72.8-89.2%, 82.5-94.8%,

30 67.7-78.6% and 84.3-88%. Density influences the carbon stock for 34.8-57%. The increase of 20 Carbon stoc carbon stock as the result of adding one value 10 on each parameter is 0.649-1.055 units from basal area, 0.763-1.063 units from density and 0 0.806-1.277 units from importance value. 0 10 20 30 40 50 60 Importance value

Brown Ketterings Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Allometric Equations Determinant coefficients Brown y = 1.021x + 1.007 R² = 0.948 Ketterings y = 0.929x + 0.199 R² = 0.912 Chave y = 1.277x + 0.825 R² = 0.893 Basuki y = 0.806x + 1.795 R² = 0.825

27 (F) Selabintana V

240 200

) 180 ) -1 -1 200 160 140 160 120

k (tons C ha k 100

k (tons C ha k 120 80 80 60 40

40 Carbon stoc Carbon stoc 20 0 0 0 20 40 60 80 0 50 100 150 200 250 300 Density (individuals ha-1) Basal area (m2 ha-1) Brown Ketterings Brown Ketterings Chave Basuki Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Linear (Chave) Linear (Basuki) Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = 0.551x + 10.73 R² = 0.734 Brown y = 1.440x - 9.722 R² = 0.408 Ketterings y = 0.493x + 9.231 R² = 0.727 Ketterings y = 1.277x - 8.847 R² = 0.398 Chave y = 0.637x + 12 R² = 0.741 Chave y = 1.652x - 11.39 R² = 0.406 Basuki y = 0.352x + 9.335 R² = 0.615 Basuki y = 0.948x - 4.289 R² = 0.364

Fig. 18. The relationship of basal area, density 250 and importance value to carbon stock on the elevation of 1710 m asl (plot Selabintana V).

) 200

-1 Basal area and importance value parameter show a relatively similar correlation value, 150 which means each parameter influences carbon

k (tons C ha k stock for 61.5-74.1% and 62.5-74.5%. Density 100 influences the carbon stock for 36.4-40.8%. The increase of carbon stock as the result of adding

Carbon stoc 50 one value on each parameter is 0.352-0.637 units from basal area, 0.948-1.625 units from

0 density and 1.125-2.028 units from importance 0 20 40 60 80 100 value. Importance value Brown Ketterings Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Allometric Equations Determinant coefficients Brown y = 1.756x + 4.750 R² = 0.74 Ketterings y = 1.569x + 3.893 R² = 0.731 Chave y = 2.028x + 5.099 R² = 0.745 Basuki y = 1.125x + 5.478 R² = 0.625

28 (G) Pasir Ipis

70 70

) 60 60

) -1 -1 50 50

40 40 k (tons C ha k k (tons C ha k 30 30

20 20 Carbon stoc Carbon stoc 10 10 0 0 0 5 10 15 20 25 30 35 40 0 10 20 30 40 50 60 Density (individuals ha-1) Basal area (m2 ha-1) Brown Ketterings Brown Ketterings Chave Basuki Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Linear (Chave) Linear (Basuki) Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = 1.176x + 4.151 R² = 0.748 Brown y = 0.605x - 0.679 R² = 0.695 Ketterings y = 0.987x + 3.140 R² = 0.746 Ketterings y = 0.49x - 0.627 R² = 0.643 Chave y = 1.432x + 4.595 R² = 0.743 Chave y = 0.710x - 0.872 R² = 0.641 Basuki y = 1.008x + 3.876 R² = 0.730 Basuki y = 0.533x - 0.486 R² = 0.716

70 Fig. 19. The relationship of basal area, density

) and importance value to carbon stock on the -1 60 elevation of 1355 m asl (plot Pasir Ipis). Basal 50 area and importance value parameter show a relatively similar correlation value, which means k (tons C ha k 40 each parameter influences carbon stock for 30 73-74.6% and 73.6-76.2%. Density influences the

Carbon stoc carbon stock for 64.1-71.6%. The increase of 20 carbon stock as the result of adding one value on 10 each parameter is 0.987-1.432 units from basal

0 area, 0.49-0.71 units from density and 0 10 20 30 40 50 60 0.611-0.886 units from importance value. Importance value

Brown Ketterings Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Allometric Equations Determinant coefficients Brown y = 0.738x + 2.250 R² = 0.762 Ketterings y = 0.611x + 1.616 R² = 0.739 Chave y = 0.886x + 2.385 R² = 0.736 Basuki y = 0.638x + 2.201 R² = 0.755

29 (H) Ciremes

350 350

300 300 ) -1 ) 250 -1 250 200 200 k (tons C ha k (tons C ha 150 150 100 100

stoc Carbon

50 Carbon stock 50 0 0 100 200 300 400 500 600 0 0 25 50 75 100 125 Basal area (m2 ha-1) Density (individuals ha-1) Brown Ketterings Brown Ketterings Chave Basuki Chave Basuki Linear (Brown) Linear (Ketterings) Linear (Brown) Linear (Ketterings) Linear (Chave) Linear (Basuki) Linear (Chave) Linear (Basuki) Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = -0.065x + 43.93 R² = 0.016 Brown y = 2.913x - 17.06 R² = 0.965 Ketterings y = -0.068x + 44.56 R² = 0.015 Ketterings y = 3.076x - 19.79 R² = 0.957 Chave y = -0.071x + 50.23 R² = 0.014 Chave y = 3.370x - 20.01 R² = 0.966 Basuki y = -0.035x + 28.51 R² = 0.012 Basuki y = 1.855x - 9.939 R² = 0.968

20. The relationship between basal area, 350 Fig. density and importance value to carbon stock on 300 )

-1 the elevation of 1271 m asl (plot Ciremes). Basal 250 area parameter shows relatively small correlation value and density shows relatively high 200

k (tons C ha k correlation value, which means density influences 150 carbon stock for 95.7-96.8%. Importance value in 100 a form of quadratic relationship to carbon stock

Carbon stoc that is able to explain 68.4-71.7% of the observed 50 variability and increases until the top of 0 importance value 60 then decreases. The increase 0 20 40 60 80 100 of carbon stock as the result of adding one value Importance value on the density is 1.855-3.370 units.

Brown Ketterings Chave Basuki Poly. (Brown) Poly. (Ketterings) Poly. (Chave) Poly. (Basuki) Allometri Equations Determinant c coefficients 2 Brown y= -0.089x + 9.230x - 34.51 R² = 0.703 Ketterings y = -0.093x2 + 9.639x - 37.66 R² = 0.684 Chave y = -0.103x2 + 10.69x - 40.70 R² =0.709 Basuki y = -0.057x2 + 5.899x - 21.66 R² = 0.717

30 (I) Ciberet

100 100 90 90 )

80 )

-1 80 -1 70 70 60 60

k (tons C ha k 50

k (tons C ha k 50 40 40 30 30 Carbon stoc

20 Carbon stoc 20 10 10 0 0 0 4 8 12 16 20 0 20 40 60 80 Density (individuals ha-1) Basal area (m2 ha-1)

Brown Ketterings Brown Ketterings Chave Basuki Chave Basuki Poly. (Brown) Poly. (Ketterings) Poly. (Brown) Poly. (Ketterings) Poly. (Chave) Poly. (Basuki) Poly. (Chave) Poly. (Basuki)

Allometric Equations Determinant Allometric Equations Determinant coefficients coefficients Brown y = -21.61x2 + 79.38x - 1.085 R² = 0.738 Brown y = 0.018x2 - 1.190x + 20.96 R² = 0.036 Ketterings y = -12.21x2 + 44.69x + 0.820 R² = 0.655 Ketterings y = 0.013x2 - 0.919x + 14.51 R² = 0.058 Chave y = -12.13x2 + 45.55x + 1.411 R² = 0.712 Chave y = 0.014x2 - 0.877x + 14.91 R² = 0.118 Basuki y = -6.989x2 + 26.99x + 2.288 R² = 0.553 Basuki y = 0.013x2 - 0.731x + 11.19 R² = 0.271

100 Fig. 21. The relationship between basal area, density and importance value to carbon stock on 90 the elevation of 1079 m asl (plot Ciremes). Basal

) 80 -1 area parameter shows quadratic relationship that 70 explains 55.3-73.8% of the observed variability, 60 and increases until the top of basal area 10 m2ha-1

k (tonsC ha k 50 then decreases. Density shows relatively small 40 correlation value. Importance value in a form of 30 cubic relationship to the carbon stock is able to Carbon stoc 20 explain 42.3-57.5% of the observed variability. 10 0 0 20 40 60 80 100 Importance value Brown Ketterings Chave Basuki Poly. (Brown) Poly. (Ketterings) Poly. (Chave) Poly. (Basuki) Allometri Equations Determinant c coefficients Brown y = 0.001x3 - 0.234x2 + 8.470x - 28.88 R² = 0.575 Ketterings y = 0.001x3 - 0.129x2 + 4.598x - 13.97 R² = 0.469 Chave y = 0.001x3 - 0.126x2 + 4.481x - 12.73 R² = 0.497 Basuki y = 0.000x3 - 0.069x2 + 2.336x - 4.542 R² = 0.423

31 3.3 Relationship between carbon stock and plant diversity

The relationship between carbon stock and plant diversity becomes an interesting thought to be observed, because it is strongly assumed there is a co-benefit between the two. This assumption occurs with the reason that both of them are resource component that provide ecosystem services. Previous studies show that at a global scale, tropical forests provide some of the highest levels of biomass carbon storage, productivity and biodiversity, spatial patterns of carbon dynamics and biodiversity are complex with limited correlations between these variables. However, plant communities in tropical forests are the fundamental units of vegetation management (UN-REDD, 2010; McPherson and DeStefano, 2003). This study will elaborate how carbon stock is related with ecological index and species formation in each ecological zone at MGPNP (Table 9, 10, 11).

Table 9. Carbon stock, ecology indexes and formation at the subalpine zone

Elevation C stock R2 to C stock Species Dominant (m asl) (ton C ha-1) BA Density Importance composition species and value Shannon Index

2802 380.549Br 0.962 0.732 0.992 Cyathea latebrosa, Vaccinium Gunung 352.844Kt 0.980 0.683 0.997 Leptospermum flavescens, varingiaefolium, Gemuruh 472.677Cv 0.976 0.704 0.990 Leptospermum javanicum, Myrsine haseltii, 368.672Bs 0.938 0.789 0.997 Macropanax concinnus, Symplocos Myrsine haseltii, odoratissima Symplocos odoratissima, Vaccinium H’: 1.409 varingiaefolium

2601 480.854Br 0.973 0.816 0.988 Ardisia javanica, Vaccinium Selabintana 466.659Kt 0.990 0.779 0.982 Leptospermum flavescens, varingiaefolium, I 635.038Cv 0.988 0.788 0.985 Myrsine hasseltii, Leptospermum 454.651Bs 0.959 0.851 0.994 Paraserianthes lophanta, flavescens, Polyosma integrifolia, Myrsine haseltii Schima wallichii, Schleffera scandens, H’:1.563 Symplocos odoratissima, Vaccinium varingiaefolium

Generally, all zones have similar tendency, which is they have positive and significant correlation between ecology indexes (basal area, density, importance value) to the carbon stock (Figure 13-21). The different ability in storing carbon at each zone is presumably related to the level of tree proximity per hectare and number of species, because in time it will affect the ecology index value. Tree proximity value per hectare accordingly from subalpine to submontane is 93, 38 and 17; meanwhile the number of species is 16, 70 and

32 44. On the subalpine zone, the correlation value (R2) is found to be very high. The average is 0.971 on basal area parameter and 0.991 on the importance value; meanwhile the average for density is 0.768. These values are higher than the ones on the montane and submontane zone. According to Bray-Curtis (Table 12), based on the community composition, the index of both elevation points on the subalpine zone has a same value as much as 79.8% and it can provide the approximate average value of the total of with this sort of formation, which is 409.751-553.858 tons C ha-1. The dominant species at this zone is Vaccinium varingiaefolium, Myrsine haseltii, Symplocos odoratissima and Leptospermum flavescens. Montane zone has 0.809 for the average of correlation value on the basal area parameter, 0.764 for importance value and 0.571 for density, and those values are seem lower than the ones on the subalpine zone. On the Bray-Curtis Index (Table 12), it is known that approximately 58-72% of species composition is different and this formation can produce the approximate of total value of carbon stock between 362.261-506.695 tons C ha-1. The dominant species at this zone is Schima wallichii, Lithocarpus sundaicus, Vernonia arborea, Platea latifolia, Neolitsea javanica, Castanopsis tungurrut and Castanopsis javanica. Submontane zone has a lack of average of correlation (R2) which is 0.474 on the basal area parameter, it is sufficient on the importance value as much as 0.734 and those values are lower than the ones on the subalpine and montane zone, except for density 0.586. Based on the Index Bray-Curtis (Table 12), about 56-97% of species composition is different and this formation can produce the average of total value of carbon stock between 219.735-304.252 tons C ha-1. The dominant species at this zone is Schima wallichii, Phoebe grandis, Castanopsis javanica, Catanopsis acuminatissima, Castanopsis tungurrut, Oriochnide scabra, Trema orientalis and Toona sureni. The composition comparison of species on the submontane zone between Ciremes and Ciberet- at Cisarua Resort-, the edge of Mount Pangrango, shows the highest difference as much as 96.9%, whereas both of them are situated in the longitude position which is relatively close 106.931 and 103.937. The lower unequal value is between Pasir Ipis and Ciberet; the longitude position of Pasir Ipis is on 106.965 (at Selabintana Resort, Gunung Gede). On the montane zone, the observation plot of longitude position is on 106.966 until 106.975. Even though with the relatively high unequal value, there is a similarity on one of the dominant species that is Schima wallichii.

33 Table 10. Carbon stock, ecology indexes and formation at the montane zone

Elevation C stock R2 to C stock Species composition Dominant (m asl) (ton C ha-1) BA Density Importance species value and Shannon Index

2329 597.560Bs 0.984 0.805 0.960 Acronychia pedunculata, Ardisia Schima Selabintana 503.441Kt 0.961 0.770 0.932 javanica, Astronia spectabilis, wallichii, II 649.765Cv 0.972 0.785 0.945 Cyathea latebrosa, Engelhardtia Lithocarpus 439.296Bs 0.973 0.808 0.954 spicata, Lithocarpus palidus, sundaicus, Lithocarpus sundaicus, Vernonia Macropanax dispermum, Myrsine arborea hasseltii, Neolitsea cassiifolia, Schima wallichii, Vernonia H’: 1.999 arborea 2075 414.383Br 0.677 0.457 0.652 Acer laurinum, Antidesma Schima Selabintana 337.339Kt 0.682 0.348 0.607 tetrandum, Astronia spectabilis, wallichii, III 423.618Cv 0.786 0.452 0.722 Cryptocarya ferrea, Cyathea Platea latifolia, 305.187Bs 0.783 0.570 0.771 latebrosa, Dacrycarpus Neolitsea imbricatus, Elaeocarpus javanica stipularis, Litsea mappacea, Macropanax dispermum, H’: 2.299 Neolitsea cassiaefolia, Neolitsea javanica, Platea latifolia, Polyosma integrifolia, Schima wallichii, Syzygium racemosum, Syzygium rostratum

1851 299.345Br 0.892 0.880 0.948 Ardisia javanica, Castanopsis Schima Selabintana 267.309Kt 0.860 0.843 0.912 javanica, Castanopsis tungurrut, wallichii, IV 347.590Cv 0.831 0.850 0.893 Dysoxylum alliaceum, Castanopsis 269.861Bs 0.728 0.870 0.825 Elaeocarpus angustifolius, tungurut, Elaeocarpus stipularis, Euonymus Castanopsis javanicus, Lithocarpus sundaicus, javanica Macropanax concinnus, Myrsine hasseltii, Neolitsea javanica, H’: 2.547 Oreochnide scabra, Platea latifolia, Polyosma integrifolia, Schima wallichii, Syzygium rostratum, Urophyllum arborescens 1710 542.575Br 0.734 0.408 0.74 Acronychia pedunculata, Ardisia Schima Selabintana 483.830Kt 0.727 0.398 0.731 javanica, Castanopsis javanica, wallichii, V 605.807Cv 0.741 0.406 0.745 Cyathea junghuniana, Cyathea Lithocarpus 434.699Bs 0.615 0.364 0.625 latebrosa, Elaeocarpus sundaicus angustifolius, Euonymus javanicus, Ficus cuspidate, H’: 2.925 Flaucortia rukam, Lagerstroemia speciosa, Lindera polyantha, Lithocarpus pallidus, Lithocarpus sundaicus, Macropanax concinnus, Magnolia lilifera, Neolitsea cassiaefolia, Neolitsea javanica, Paraserianthes lophanta, Platea latifolia, Polyosma integrifolia, Schima wallichii, Syzygium antisepticum, Syzygium rostratum, Turpinia sphaerocarpa, Weinmannia blumei

34 Table 11. Carbon stock, ecology indexes and formation at the submontane zone

Elevation C stock R2 to C stock Species Dominant (m asl) (tons C ha-1) composition species and BA Density Importance Shannon Index value 1355 233.843Br 0.748 0.695 0.762 Acronychia pedunculata, Schima Pasir Ipis 197.770Kt 0.746 0.643 0.739 Antidesma tentrandum, wallichii, 260.616Cv 0.743 0.641 0.736 Castanopsis javanica, Phoebe grandis, 217.077Bs 0.730 0.716 0.755 Dysoxylum alliaceum, Castanopsis Elaeocarpus stipularis, javanica Euonymus javanicus, Ficus heterophylla, H’: 2.701 Gordonia excelsa, Itea macrophylla, Lagerstroemia indica, Lindera polyantha, Lithocarpus pallidus, Lithocarpus sundaicus, Macropanax dispermum, Neolitsea cassiaefolia, Neolitsea javanica, Phoebe grandis, Prunus arboreum, Rapanea hasseltii, Schima wallichii, Symplocos odoratissima, Urophyllum arboreum, Ziziphus angustifolia

1271 406.290Br 0.016 0.965 0.965 Achronisia trifoliolata, Catanopsis Ciremes 410.322Kt 0.015 0.957 0.957 Alangium chinense, acuminatissima, 459.758Cv 0.014 0.966 0.966 Castanopsis tungurut, Castanopsis 285.298Bs 0.012 0.968 0.968 Catanopsis tungurut, acuminatissima, Cyathea Schima wallichii latebrosa, Elaeocarpus angustifolius, Ficus H’: 1.415 ribes, Schima wallichii

1079 242.113Br 0.738 0.036 0.575 Aglaia ecliptica, Cyathea Oriochnide Ciberet 172.379Kt 0.655 0.058 0.469 spinulosa, Ficus scabra, Trema 192.383Cv 0.712 0.118 0.497 fistulosa, Ficus orientalis, 156.830Bs 0.553 0.271 0.423 heterophyla, Ficus Toona sureni padana, Laportea stimulans, Lithocarpus H’:1.892 sundaicus, Oriochnide scabra, Pinanga coronata, Toona sureni, Trema orientalis, Vernonia arborea

35 Table 12. Bray-Curtis Index

Zone Site X2-X1 X1+X2 B 1-B TOTAL TOTAL Submontane Ciremes-Ciberet 63 65 0.969 0.031 Pasir Ipis-Ciberet 47 83 0.566 0.434 Pasir Ipis-Ciremes 92 127 0.724 0.276 Montane Selabintana III-Selabintana II 101 141 0.716 0.284 Selabintana IV-Selabintana II 108 140 0.771 0.229 Selabintana V-Selabintana II 107 175 0.611 0.389 Selabintana IV-Selabintana III 97 141 0.688 0.312 Selabintana V-Selabintana III 116 170 0.682 0.318 Selabintana V-Selabintana IV 99 169 0.586 0.414 Subalpine Gunung Gemuruh-Selabintana I 45 223 0.202 0.798 Note: B: Dissimilarity Bray-Curtis Index 1-B: Similarity Bray-Curtis Index which is complement B If 1-B = 1, then two communities are the same 1-B = 0, then two communities are different.

There are ten out of 66 species that have the highest carbon value on the 4 allometric equations with the value range in tons C ha-1 between 79.146-652.434 (Br), 60.159-581.317 (Kt), 76.519-772.624 (Cv) and 61.497-451.682 (Bs) (Table 13 and Figure 22). The carbon stock value on this each species is a total calculation of carbon stock of all individuals found on all observation plots. It is an example of carbon stock based on dominant species on an ecosystem. The maximum value is seen on Schima wallichii (Br, Kt, Cv, Bs) and the minimum value is different for each allometric equation, that is Neolitsea cassiaefolia (Br) and Cyathea junghuhniana (Kt) except Platea latifolia (Cv and Bs). This top 10 species is dominant species on certain elevations, except Neolitsea cassiaefolia and Cyathea junghuhniana as a result of different calculation result using allometric equation. This calculation result can be a recommendation in determining priority species for conservation purpose and the carbon stock increase in recovering degradated fields and reforestation on mountains ecosystem with wet climate.

36 Table 13. Highest ten species in carbon stock value on 4 allometric equations

Carbon stock (tons C ha-1) Species Brown Ketterings Chave Basuki Schima wallichii 652.434 581.317 772.624 451.682 Vaccinium varingiaefolium 379.925 361.938 514.088 346.264 Castanopsis tungurrut 317.918 334.107 374.925 209.351 Lithocarpus sundaicus 254.612 197.629 270.991 166.498 Leptospermum flavescens 127.896 142.800 199.277 116.576 Platea latifolia 96.328 - 76.516 61.497 Myrsine hasseltii 93.943 85.970 126.022 104.398 Toona sureni 91.688 - - - Symplocos odoratissima 83.935 60.494 86.232 83.466 Neolitsea cassiaefolia 79.146 71.690 101.100 64.969 Castanopsis javanica - 66.255 96.563 73.225 Cyathea junghuhniana - 60.159 - -

Some of previous studies also have revealed the relationship between Shannon Index and carbon stock, some of which have negative correlation -0.25 in Garhwal Himalaya, India (Sharma et al., 2010), there was no straightforward relationship between biodiversity of perennial plant species growing in a certain land use units and the stock of C in their aboveground biomass in western Kenya (Henry et al., 2009), there was unable to find any evidence for a relationship between tree species diversity and aboveground carbon stocks in eastern Panama (Kirby and Potvin, 2007), but on the other hand, the soil C stock was directly related to plant diversity of homegardens in Kerala, India (Saha et al., 2009). The relationship between Shannon Index and carbon stock is modeled in cubic polynomial regression equation, because of the non-linear data spread pattern (Figure 23). However, this model results on the relatively small correlation value (R2 = 0.236-0.284). The results of the equation are y = 686.5x3 - 4349x2 + 8854x – 5375 (Brown), y = 542.2x3 - 3335x2 + 6543x – 3730 (Ketterings), y = 694.8x3 - 4287x2 + 8448x - 4852 (Chave) and y = 434.9x3 - 2668x2 + 5233x - 2950 (Basuki). The model using interval confidence of 95% and carbon stock value on particular Shannon Index are in the interval of lower mean and upper mean. Further research is required in order to reveal the relation between both of these variables so that the appropriate model development is obtained.

37 900

Brown 800 Ketterings Chave 700 Basuki

600

500

400

300

200

100

0 … … … … … … … s is ut sis ocos inium inium ribes stifolia padana laurinum fistulosa spermum spermum stimulans a wallichii a arborea olia olia lilifera a hasseltii cuspidata acrophylla Vacc aeocarpus Ficus Sympl Catanop sia javanica ebe grandis atea latifolia aia aia eclliptica El oemia indica oemia heterophylla sine hasseltii sine Toona sureni Ficus era scandens itsea itsea javanica donia excelsa donia Acer Pl orientalis ema Ficus llum arboreum llum Lepto Lepto ptocarya ferrea ptocarya Paraserianthe eocnide scabraeocnide tsea mappacea Agl latebrosa athea Magn Prunus arborea Ficus athea spinulosa athea speciosaoemia nanga coronata Ardi a sphaerocarpa Pho Schim Tr ndera polyantha rostratumygium inmannia inmannia blumei elhardtia elhardtia spicata Itea m Myr nymus javanicus Flacourtia rukam chia pedunculata chia thocarpus pallida Li angium angium chinense itsea cassiaefolia Vernoni yosma integrifolia Gor Pi opanax cocinnumopanax Or Li Rapane onychia trifoliolata onychia idesma idesma tetrandum Ficus Cy Neol soxylum alliaceum soxylum ygium racemosum Cy Cry ycarpus imbricatus ycarpus Li Laportea Al ygium antisepticum ygium opanax dispermum opanax Astronia spectabil thocarpus sundaica athea junghuhniana athea We Syz aeocarpus stipularis Ziziphus angu Eng Scheffl Euo Pol Lagerstr Dy Castanopsis javanica Li Acr Ant Urophy Neol Castanopsis tungurr El Syz Cy Turpini Macr Syz Dacr Acrony Lagerstr Macr

Figure 22. Carbon stock of plant species at the observation plot on Mount Gede Pangrango National Park

38 700 600 )

-1 600 ) 500 -1 500 400 400 300 300 200 rbon rbon stock (tons C ha 200 rbon stock (tons ha Ca y = 686,5x3 - 4349,x2 + 8854,x - 5375, Ca 100 R² = 0,261 100 y = 542,2x3 - 3335,x2 + 6543,x - 3730, R² = 0,284 0 0 0,00 1,00 2,00 3,00 4,00 0,00 1,00 2,00 3,00 4,00 Shannon index Shannon index Brown Prediction Poly. (Brown) Ketterings Prediction Poly. (Ketterings)

700 500 450 600 ) ) -1 -1 400 500 350 300 400 250 300 200

200 150 rbon rbon stock (tons C ha rbon rbon stock (tons C ha 100 Ca Ca 3 2 3 2 100 y = 694,8x - 4287,x + 8448,x - 4852, y = 434,9x - 2668,x + 5233,x - 2950, R² = 0,248 50 R² = 0,236 0 0 0,00 1,00 2,00 3,00 4,00 0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 Shannon index Shannon index

Chave Prediction Poly. (Chave) Basuki Prediction Poly. (Basuki)

Figure 23. Model of cubic polynomial regression for correlation between Shannon Index and carbon stock on 4 allometric equations.

39 Table14. The approach of interval value test for carbon stock on the model of cubic polynomial regression

Shannon Shannon2 Shannon3 Brown LMCI UMCI Ketterings LMCI UMCI Chavez LMCI UMCI Basuki LMCI UMCI 1.4089 1.985 2.7967 380.549 210.323 651.096 352.844 217.369 618.434 472.677 254.717 796.009 368.672 194.311 544.541

1.5633 2.4439 3.8206 480.854 246.849 577.389 466.659 231.316 532.080 635.038 279.062 684.983 454.651 209.895 472.537

1.9992 3.9968 7.9904 597.560 193.658 561.530 503.441 146.061 480.794 649.765 174.425 626.191 439.296 143.862 436.167

2.2999 5.2895 12.1654 414.383 181.913 562.105 337.339 127.809 473.751 423.618 151.749 618.644 305.187 132.518 434.611

2.5468 6.4862 16.519 299.345 211.461 549.374 267.309 161.215 468.687 347.590 194.710 609.685 269.861 164.597 433.097

2.9253 8.5574 25.0329 542.575 119.341 718.938 483.83 112.689 658.275 605.807 118.548 854.884 434.699 124.966 601.396

2.7012 7.2965 19.7093 233.843 211.179 573.308 197.77 171.466 500.973 260.616 205.334 650.047 217.077 174.934 462.675

1.415 2.0022 2.8331 406.290 212.482 647.354 410.322 218.515 614.216 459.758 256.499 790.544 285.298 195.450 540.992

1.8924 3.5812 6.777 242.113 212.010 554.712 172.379 169.153 480.983 192.383 203.808 624.665 156.830 162.143 434.449

Shannon Index is created until order 3. LMCI: Lower mean confidence interval, UMCI: Upper mean confidence interval. Interval confidence 95%. Value Y (carbon stock) as dependent variables are between LMCI and UMCI.

40 3.4 Carbon stock and plant diversity as basis of an ecosystem services model

The ability of nature forest ecosystem MGPNP in supporting carbon can be observed from its three ecology zones. The result of the research shows that the average calculation from 4 allometric equations in millions of tons C is 8.33 (Br), 8.043 (Kt), 10.07 (Cv) and 7.235 (Bs) (Table 15). The maximum value of montage zone in millions of tons C is 6.463 (Br), 5.549 (Kt), 7.066 (Cv) and 5.052 (Bs). Meanwhile, the minimum value of subalpine zone is 0.507 (Br), 0.483 (Kt), 0.652 (Cv) and 0.485 (Bs). It is reasonable as the montane zone is the widest zone, thus, this zone management needs a primary attention. Carbon stock is in biomass, which constructed from plant diversity, understorey, litter, necromass and soil carbon. This calculation can be a basis for carbon sequestration through time series observation through the years.

Table 15. Estimation of carbon stock on the nature forest MGPNP Ecological Width Carbon stock average (tons C) zone (hectare) Brown Ketterings Chave Basuki

Subalpine 1177.81 507284.3225 482609.2118 652339.1745 484859.0294 Montane 13945.43 6463232.89 5549999.466 7066081.41 5051883.214 Submontane 7727.787 2272602.02 2010438.88 2351197.947 1698067.834

Total 22851.03 9243119.23 8043047.558 10069618.53 7234810.078

The positive and significant pattern of the relation between plant diversity and carbon stock in this study is important basic information in creating ecosystem services structure. Ecosystem services are the benefits provided by ecosystems such as supporting, provisioning, regulating and cultural for constituents of well being (Millennium Ecosystem Assessment, 2005). In this case, forest ecosystem plays a key role in the provision of ecosystem services, one of which as carbon sinks providing climate regulating services, thereby reducing the rate of increase of atmospheric CO2 (Brockhaus and Botoni, 2009; Lewis et al., 2009). It is proven because biomass contributes 99% to the total of carbon stock (see Appendix 5). The interaction of ecological index from plant diversity and service of carbon stock is an use value. In the context of total economic value, this use value will complement the non-use value to provide conservation and development benefit (Figure 24).

41 The economic valuation of ecosystem services is a key policy tool in stemming losses of biological diversity (Abson and Termansen, 2010). This kind of framework of ecosystem services can be a basis of strategy structuring of Low Carbon Development, which is eventually aimed for local people welfare at Cibodas Biosphere Reserve, which can also provide a global result. On a global scale, tropical forests provide some of the highest levels of biomass carbon storage, productivity and biodiversity (UN-REDD, 2010). Biodiversity propose as an “insurance” or buffer, against environmental fluctuations, because different species respond differently to these fluctuations, leading to more predict-

Cibodas Biosphere Reserve

M Mount Gede Pangrango National Park Plant Diversity (Nature forest ecosystem) ( 1 ) Ecological Index

 Basal Area Carbon Stock  Density Correlation (+) Storage  Importance Above & below Significant Value ground ( 2 ) Shannon Index ( 3 ) Diversity Index

Global Policy National Policy  Biosphere  Insitu Reserve conservation  REDD+  Mitigation Total economic value  CBD ( Reducing Green  Conservation house gas : 26 % )  CCC  Development  GSPC

Stokeholders Stakeholders Benefit  Research Instution Benefit  Government  Schools/University  International  Non Government  Private Organization

Low Carbon Development for Society

Figure 24. Ecosystem services model on Mount Gede Pangrango National Park based on carbon stock and plant diversity. Note:  : Bioresources,  : Cibodas Biosphere Reserve as a part of global ecosystem, : Policy and stakeholders, : Output and  : Target

42 able ecosystem properties (Loreau et al., 2001). The management of in situ conservation area that provides a space for optimum regeneration of native species, together with the strengthening of its local people and stakeholder's capacity, will keep the mitigation function through carbon stock of forest stand. The instrument of national regulations of in situ conservation area (SK Menteri Kehutanan Nomor 174/Kpts-II/2003) and the decrease of green house gas emission (Perpres Nomor 61 Tahun 2011) becomes a strong foundation in taking steps of measurement, reporting and verification (MRV). Credible MRV can strengthen mutual confidence in countries actions and in the regime, thereby enabling a stronger collective effort (Breidenich and Bodansky, 2009). On a global scale, the sanction of Cibodas Biosphere Reserve with its three strategic functions – conservation, development, logistic – (UNESCO, 2011) is able to correspond the existence of its core area (MGPNP) for implementation of CBD (Convention on Biological Diversity), CCC (Convention on Climate Change), GSPC (Global Strategy for Plant Conservation) and REDD plus (Reducing Emissions from Deforestation and Forest Degradation). The main idea of the overall regulations is sustainable biodiversity conservation and its use with the holistic point of view based on the ecosystem balance. The principles of ecosystem balance consist of harmony, ever last, recovery, homeostasis, efficiency and integrity. The rules of connection and dependency of ecosystem is reflected harmonically in a balance between life elements towards clean environment (Low Carbon Development). The recommended actions in executing the major mission are as follow: Recommendation 1.To do a continuous research for carbon stock measurement and carbon sequestration that is cooperating with competent research institutions. The scientific findings about native species characteristic and its ability in supporting carbon become valuable information for the good of the conservation. Recommendation 2. To construct a database of plant diversity and its use in order to be evaluated so that it can answer scientifically the REDD plus application as well as to pay special attention to species in red list. For this matter, high precision is required to accumulate the functions of ecology. Recommendation 3. The development and socialization regarding Low Carbon Development on the stakeholders in order to find an understanding and support in the efforts of conserving the area and keeping its sustainable benefits.

43 4.0 CONCLUSION Carbon stock has a positive and significant correlation towards plant diversity on Mount Gede Pangrango National Park as the core zone of Cibodas Biosphere Reserve. It is shown from the approximate R2 values from ecological index used between 0.571-0.991. There are 13 top species top that have the abilities to support the highest carbon; those are Schima wallichii, Vaccinium varingiaefolium, Castanopsis tungurrut, Lithocarpus sundaicus, Leptospermum flavescens, Platea latifolia, Myrsine hasseltii, Toona sureni, Symplocos odoratissima, Neolitsea cassiaefolia, Castanopsis javanica, Cyathea junghuhniana. Schima wallichii is found widely spread from submontane until subalpine zone with the frequency of 0.778 and importance value of 36.865. The difference in species composition is relatively high on each observation plot on montane and submontane zone (56-97%), meanwhile on the subalpine zone is relatively low (20%). Shannon Index has maximum value of 2.925 and minimum value of 1.409. The trees contribution is biggest one for total carbon stock, which is 79.766% (Basuki)- 84.994% (Chave); litter is 11.778% (Chave)-15.735% (Basuki); necromass is 3.156% (Chave) – 4.117% (Basuki); and understorey is 1.198% (Chave) – 1.559% (Basuki). The approximate of the average contribution from big trees for aboveground carbon stock is 42.697-51.963% and from small trees is 29.664-35.892%. The ability of carbon stock MGPNP in million tons C is on the montane zone with maximum value of 6.463 (Br), 5.549 (Kt), 7.066 (Cv) and 5.052 (Bs), meanwhile the minimum value on the subalpine zone is 0.507 (Br), 0.483 (Kt), 0.652 (Cv) and 0.485 (Bs). Based on the conservation value aspect, special attention is needed for species that includes into IUCN red list; some of them are Euonymus javanicus, Dysoxylum alliaceum, Engelhardtia spicata, Macropanax concinnus and Prunus arborea. The approach of ecosystem services model based on carbon stock and plant diversity can be a foundation for structuring strategy of Low Carbon Development. It is eventually aimed for the welfare of local people at Cibodas Biosphere Reserve, which will give a global outcome. Recommendations comprise sustainable research about carbon stock measurement and carbon sequestration, building a database of plant diversity and its use, also a uniformity of perspectives and actions for Low Carbon Development.

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48 APPENDICES

APPENDIX 1

List of species and carbon stock on study site No Species Family Elevation Carbon stock (tons C ha-1) (masl) Zone Brown Ketterings Chave Basuki 1 Acer laurinum Sapindaceae 2075 M 1.786 1.229 1.813 1.312 2 Acronychia trifoliolata Rutaceae 1271 SM 1.623 1.402 2.071 1.464 3 Acronychia pedunculata Rutaceae 2329; 1710; 1355 M; SM 14.960 11.647 17.173 16.434 4 Aglaia eclliptica Meliaceae 1079 SM 4.589 4.947 7.063 4.193 5 Alangium chinense Cornaceae 1271 SM 2.553 1.384 2.023 1.450 6 Antidesma tetrandum Euphorbiaceae 2075; 1355 M; SM 0.965 0.811 1.173 1.519 2601; 2329; 1851; 7 Ardisia javanica Primulaceae 1710 SA; M 6.312 4.540 6.659 6.665 8 Astronia spectabilis Melastomataceae 2329; 2075; M 15.406 11.247 16.033 10.908 9 Castanopsis javanica Fagaceae 1851; 1710; 1355 M; SM 77.307 66.255 96.563 73.225 10 Castanopsis tungurrut Fagaceae 1851; 1271 M; SM 317.918 334.107 374.925 209.351 11 Catanopsis acuminatissima Fagaceae 1271 SM 7.988 7.519 11.184 8.696 12 Cryptocarya ferrea Lauraceae 2075 M 0.253 0.216 0.311 0.448 13 Cyathea junghuhniana Cyatheaceae 1710 M 60.159 60.159 60.159 60.159 2802; 2329; 2075; SA; M; 14 Cyathea latebrosa Cyatheaceae 1710; 1271 SM 55.923 55.923 55.923 55.923 15 Cyathea spinulosa Cyatheaceae 1079 SM 22.951 22.951 22.951 22.951 16 Dacrycarpus imbricatus Podocarpaceae 2075 M 66.582 50.477 53.445 29.213 17 Dysoxylum alliaceum Meliaceae 1851; 1355 M; SM 17.462 15.288 22.097 17.654 18 Elaeocarpus angustifolius Elaeocarpaceae 1851; 1710; 1271 M; SM 32.600 19.620 28.877 23.677 19 Elaeocarpus stipularis Elaeocarpaceae 2075; 1851; 1355 M; SM 32.636 22.023 32.162 26.387 20 Engelhardtia spicata Juglandaceae 2329 M 0.991 0.590 0.861 1.040 21 Euonymus javanicus Celastraceae 1851; 1710; 1355 M; SM 10.978 8.603 12.687 12.580 22 Ficus cuspidata Moraceae 1710 M 0.541 0.315 0.454 0.689 23 Ficus fistulosa Moraceae 1079 SM 3.215 1.812 2.683 2.654 24 Ficus heterophylla Moraceae 1355; 1079 SM 6.280 3.151 4.678 4.405 25 Ficus padana Moraceae 1079 SM 1.857 0.829 1.222 0.946 26 Ficus ribes Moraceae 1271 SM 0.523 0.270 0.391 0.542 27 Flacourtia rukam Salicaceae 1710 M 0.557 0.583 0.844 1.028 28 Gordonia excelsa Theaceae 1355 SM 1.814 1.504 2.217 1.552 29 Itea macrophylla Iteaceae 1355 SM 0.837 0.379 0.548 0.804 30 Lagerstroemia indica Lythraceae 1355 SM 5.818 4.306 6.339 5.225 31 Lagerstroemia speciosa Lythraceae 1710 M 10.155 9.500 12.870 7.235 32 Laportea stimulans Urticaceae 1079 SM 0.321 0.103 0.148 0.242 33 Leptospermum flavescens Myrtaceae 2802; 2601 SA 127.896 142.800 199.277 116.576 34 Leptospermum javanicum Myrtaceae 2802 SA 14.601 14.942 2.519 2.858 35 Lindera polyantha Lauraceae 1710; 1355 M; SM 6.491 3.751 5.502 3.984 36 Lithocarpus pallidus Fagaceae 2329; 1710; 1355 M; SM 17.302 18.273 25.947 15.457 2329; 1851; 1710; 37 Lithocarpus sundaicus Fagaceae 1355; 1079 M; SM 254.612 197.629 270.991 166.498 38 Litsea mappacea Lauraceae 2075 M 9.280 5.442 7.861 5.393 Note: SA is subalpine; M is montane; SM is submontane

49 APPENDIX 1 (continues) List of species and carbon stock on study site No Species Family Elevation Zona Carbon stock (tons C ha-1) (masl) Brown Ketterings Chave Basuki

39 Macropanax concinnus Araliaceae 2802; 1851; 1710 SA; M 16.549 7.786 11.525 11.560

40 Macropanax dispermum Araliaceae 2329; 2075; 1355 M; SM 22.346 10.475 15.491 15.773

41 Magnolia lilifera Magnoliaceae 1710 M 0.685 0.443 0.643 0.818 42 Myrsine hasseltii Primulaceae 2802; 2601; 2329; SA; M 93.943 85.970 126.022 104.398 1851 43 Neolitsea cassiaefolia Lauraceae 2329; 2075; 1710; M; SM 79.146 71.690 101.100 64.969 1355 44 Neolitsea javanica Lauraceae 2075; 1851; 1710; M; SM 47.880 40.409 59.087 43.484 1355 45 Oreocnide scabra Urticaceae 1851; 1079 M; SM 27.434 18.874 27.820 27.296

46 Paraserianthes Fabaceae 2601; 1710 SA; M 1.048 1.048 1.048 1.048 lophantha 47 Phoebe grandis Lauraceae 1355 SM 6.992 4.176 6.207 5.479 48 Pinanga coronata Arecaceae 1079 SM 22.615 17.147 24.668 19.401 49 Platea latifolia Icacinaceae 2075; 1851; 1710 M 96.328 52.300 76.516 61.497 50 Polyosma integrifolia Escalloniaceae 2601; 2075; 1851; M 12.489 9.026 13.232 13.204 1710 51 Prunus arborea Rosaceae 1355 SM 25.362 21.441 31.349 21.019 52 Rapanea hasseltii Primulaceae 1355 SM 2.110 2.083 3.061 2.037 2601; 2329; 2075; SA; M; 53 Schima wallichii Theaceae 1851; 1710 ; 1355; SM 652.434 581.317 772.624 451.682 1271 54 Schefflera scandens Araliaceae 2601 SA 0.253 0.118 0.170 0.271

55 Symplocos odoratissima Symplocaceae 2802; 2601 SA 83.935 60.494 86.232 83.466

56 Syzygium antisepticum Myrtaceae 1710 M 12.412 9.830 13.086 7.448

57 Syzygium racemosum Myrtaceae 2075 M 23.934 28.824 40.316 22.747 58 Syzygium rostratum Myrtaceae 2075; 1851; 1710 M 38.506 44.741 65.349 48.864 59 Toona sureni Meliaceae 1079 SM 91.688 52.428 52.134 30.180 60 Trema orientalis Cannabaceae 1271; 1079 SM 14.729 7.713 10.605 6.796 61 Turpinia sphaerocarpa Staphyleaceae 1710 M 0.873 0.439 0.634 0.911

62 Urophyllum arboreum Rubiaceae 1851; 1355 M; SM 1.464 0.803 1.169 1.480

63 Vaccinium Ericaceae 2802; 2601 SA 379.925 361.938 514.088 346.264 varingiaefolium 64 Vernonia arborea Asteraceae 2329 M 27.969 14.537 21.443 17.753 65 Weinmannia blumei Cunoniaceae 1710 M 4.184 3.469 5.149 4.560 66 Ziziphus angustifolia Rhamnaceae 1355 SM 0.232 0.196 0.283 0.414 Note: SA is subalpine; M is montane; SM is submontane

50

APPENDIX 2 Bray-Curtis index calculation on sub montane zone No Species Number of individual per plot (X) X2-X1 X2+X1 XCB XCR XPI XCR-XCB XPI-XCB XPI-XCR XCR+XCB XPI+XCB XPI+XC 1 Acronychia trifoliolata 1 1 0 1 1 0 R1 2 Acronychia pedunculata 5 0 5 5 0 5 5 3 Aglaia eclliptica 1 1 1 0 1 1 0 4 Alangium chinense 1 1 0 1 1 0 1 5 Antidesma tentrandum 1 0 1 1 0 1 1 6 Castanopsis acuminatissima 1 1 0 1 1 0 1 7 Castanopsis javanica 11 0 0 11 0 11 11 8 Castanopsis tungurrut 20 20 0 20 20 0 20 9 Cyathea latebrosa 1 1 0 1 1 0 1 10 Cyathea spinulosa 1 1 1 0 1 0 1 11 Dysoxylum alliaceum 2 0 2 2 0 2 2 12 Elaeocarpus angustifolius 2 2 0 2 2 0 2 13 Elaeocarpus stipularis 5 0 0 5 0 5 5 14 Euonymus javanicus 3 0 0 3 0 3 3 15 Ficus fistulosa 1 1 1 0 1 0 1 16 Ficus heterophylla 1 1 1 0 1 1 2 1 17 Ficus padana 2 2 2 0 2 2 0 18 Ficus ribes 1 1 0 1 1 0 1 19 Gordonia excelsa 1 0 0 1 0 1 1 20 Itea macrophylla 2 0 0 2 0 2 2 21 Lagerstroemia indica 2 0 0 2 0 2 2 22 Laportea stimulans 1 1 1 0 1 0 1 23 Lindera polyantha 1 0 0 1 0 1 1 24 Lithocarpus pallidus 1 0 0 1 0 1 1 25 Lithocarpus sundaicus 1 1 1 0 1 1 2 1 26 Macropanax dispermum 6 0 0 6 0 6 6 27 Neolitsea cassiaefolia 1 0 0 1 0 1 1 28 Neolitsea javanica 1 0 0 1 0 1 1 29 Oreocnide scabra 14 14 14 0 14 0 14 30 Phoebe grandis 11 0 0 11 0 11 11 31 Pinanga coronata 2 2 2 0 2 2 2 32 Prunus arboreum 1 0 0 1 0 1 1 33 Rapanea hasseltii 1 0 0 1 0 1 1 34 Schima wallichii 7 11 7 11 4 7 11 18 35 Symplocos odoratissima 1 0 0 1 0 1 1 36 Toona sureni 1 1 1 0 1 0 1 37 Trema orientalis 4 1 3 4 1 5 4 1 38 Urophyllum arboreum 1 0 0 1 0 1 1 39 Vernonia arborea 1 1 1 0 1 1 1 40 Ziziphus angustifolia 1 0 0 1 0 1 1 TOTAL 30 35 71 63 47 92 65 83 127 Note: Study site is shown by CB: Ciberet, CR: Ciremes and PI: Pasir Ipis

51

APPENDIX 2 (continues) Bray-Curtis index calculation on montane zone Number of individual X2-X1 X2+X1 perXSB plot XSB (X) XSB XSB XSBIII- XSBIV- XSBV XSBIV- XSBV XSBV XSBIII+ XSBIV+ XSBV+ XSBIV+ XSB XSBV+ No Species II III IV V XSBII XSBII -XSB II XSBIII -XSB II -XSB I XSBII XSBII XSBII XSBIII V+XSB XSBIV 1 Acer laurinum 1 1 0 0 1 I1 V0 1 0 0 1 I1II 0 2 Acronychia pedunculata 9 1 9 9 8 0 1 1 9 9 10 0 1 1 3 Antidesma tetrandum 1 1 0 0 1 1 0 1 0 0 1 1 0 4 Ardisia javanica 1 3 2 1 2 1 3 2 1 1 4 3 3 2 5 5 Astronia spectabilis 2 4 2 2 2 4 4 0 6 2 2 4 4 0 6 Castanopsis javanica 7 3 0 7 3 7 3 4 0 7 3 7 3 10 7 Castanopsis tungurrut 7 0 7 0 7 0 7 0 7 0 7 0 7 8 Cryptocarya ferrea 1 1 0 0 1 1 0 1 0 0 1 1 0 9 Cyathea junghuniana 4 0 0 4 0 4 4 0 0 4 0 4 4 10 Cyathea latebrosa 2 2 5 0 2 3 2 3 5 4 2 7 2 7 5 11 Dacrycarpus imbricatus 1 1 0 0 1 1 0 1 0 0 1 1 0 12 Dysoxylum alliaceum 5 0 5 0 5 0 5 0 5 0 5 0 5 13 Elaeocarpus angustifolius 7 6 0 7 6 7 6 1 0 7 6 7 6 13 14 Elaeocarpus stipularis 1 6 1 6 0 5 1 6 1 6 0 7 1 6 15 Engelhardtia spicata 1 1 1 1 0 0 0 1 1 1 0 0 0 16 Euonymus javanicus 1 7 0 1 7 1 7 6 0 1 7 1 7 8 17 Ficus cuspidata 8 0 0 8 0 8 8 0 0 8 0 8 8 18 Flaucortia rukam 9 0 0 9 0 9 9 0 0 9 0 9 9 19 Lagerstroemia speciosa 1 0 0 1 0 1 1 0 0 1 0 1 1 20 Lindera polyantha 2 0 0 2 0 2 2 0 0 2 0 2 2 21 Lithocarpus pallidus 1 2 1 1 1 0 2 2 1 1 3 0 2 2 22 Lithocarpus sundaicus 15 2 7 15 13 8 2 7 5 15 17 22 2 7 9 23 Litsea mappacea 3 3 0 0 3 3 0 3 0 0 3 3 0 24 Macropanax concinnus 2 5 0 2 5 2 5 3 0 2 5 2 5 7 25 Macropanax dispermum 3 3 0 3 3 3 3 0 6 3 3 3 3 0 26 Magnolia lilifera 1 0 0 1 0 1 1 0 0 1 0 1 1 27 Myrsine hasseltii 1 1 1 0 1 1 0 1 1 2 1 1 0 1 28 Neolitsea cassiaefolia 5 1 5 4 5 0 1 4 5 6 5 10 1 6 5 29 Neolitsea javanica 13 1 6 13 1 6 12 7 5 13 1 6 14 19 7 30 Oreocnide scabra 1 0 1 0 1 0 1 0 1 0 1 0 1 31 Paraserianthes lophanta 1 0 0 1 0 1 1 0 0 1 0 1 1 32 Platea latifolia 16 4 3 16 4 3 12 13 1 16 4 3 20 19 7 33 Polyosma integrifolia 7 2 1 7 2 1 5 6 1 7 2 1 9 8 3 34 Schima wallichii 20 12 12 14 8 8 6 0 2 2 32 32 34 24 26 26 35 Syzygium antisepticum 1 0 0 1 0 1 1 0 0 1 0 1 1 36 Syzygium racemosum 3 3 0 0 3 3 0 3 0 0 3 3 0 37 Syzygium rostratum 2 8 2 2 8 2 6 0 6 2 8 8 10 4 10 38 Turpinia sphaerocarpa 2 0 0 2 0 2 2 0 0 2 0 2 2 39 Urophyllum arboreum 1 0 1 0 1 0 1 0 1 0 1 0 1 40 Vernonia arborea 10 10 10 10 0 0 0 10 10 10 0 0 0 41 Weinmannia blumei 1 0 0 1 0 1 1 0 0 1 0 1 1 TOTAL 70 71 70 99 101 108 107 97 116 99 141 140 175 141 170 169 Note: Study site is shown by SB II-V: Selabintana II-V

52

APPENDIX 2 (continues)

Bray-Curtis index calculation on subalpine zone Number of individual per plot X2-X1 X2+X1 No Species XGM XSBI XSBI-XGM XSBI+XGM

1 Ardisia javanica 1 1 1 2 Cyathea latebrosa 1 1 1 3 Leptospermum flavescens 4 23 19 27 4 Leptospermum javanicum 3 3 3

5 Macropanax concinnus 2 2 2

6 Myrsine hasseltii 29 26 3 55 7 Paraserianthes lophanta 1 1 1 8 Polyosma integrifolia 1 1 1 9 Schima wallichii 2 2 2

10 Schleffera scandens 1 1 1 11 Symplocos odoratissima 29 19 10 48 12 Vaccinium varingiaefolium 41 40 1 81 TOTAL 109 114 45 223

Note: Study site is shown by GM: Gunung Gemuruh, SB I: Selabintana I

53 APPENDIX 3

Shannon Index, number of species and highest importance value on study site

Number Site Elevation Zone Shannon Highest importance value of (masl) Index species

Gunung 2802 Sub Alpin 7 1.409^ Vaccinium varingiaefolium 121.215

Gemuruh Myrsine hasseltii 36.301

Symplocos odoratissima 32.797

Selabintana I 2601 Sub Alpin 9 1.563 Vaccinium varingiaefolium 89.940

Leptospermum flavescens 56.172

Myrsine hasseltii 29.784

Selabintana II 2329 Montana 12 1.999 Schima wallichii 81.774

Lithocarpus sundaicus 59.069

Vernonia arborea 21.274

Selabintana III 2075 Montana 16 2.300 Schima wallichii 64.538

Platea latifolia 49.593

Neolitsea javanica 33.273

Selabintana IV 1851 Montana 17 2.547 Schima wallichii 53.363

Castanopsis tungurrut 32.369

Castanopsis javanica 20.593

Selabintana V 1710 Montana 25 2.925* Schima wallichii 91.645

Lithocarpus sundaicus 22.046 PasirIpis 1355 Sub 23 2.701 Montana Schima wallichii 42.386

Phoebe grandis 24.987

Castanopsis javanica 20.191 Ciremes 1271 Sub 9 1.415 Montana Castanopsis acuminatissima 95.206

Castanopsis tungurrut 57.179

Schima wallichii 27.067 Ciberet 1079 Sub 12 1.892 Montana Oreocnide scabra 78.753

Trema orientalis 52.014

Toona sureni 23.138 Note: * is maximum value and ^ is minimum value

54 APPENDIX 4

Species density at the observation plots on Mount Gede Pangrango National Park Species Density Species Density (individuals ha-1) (individuals ha-1) Vaccinium varingiaefolium 405 Lindera polyantha 15 Schima wallichii 390 Litsea mappacea 15 Myrsine hasseltii 285 Syzygium racemosum 15 Symplocos odoratissima 245 Antidesma tetrandum 10 Castanopsis tungurrut 135 Ficus heterophylla 10 Leptospermum flavescens 135 Ficus padana 10 Lithocarpus sundaicus 125 Itea macrophylla 10 Platea latifolia 115 Lagerstroemia indica 10 Castanopsis javanica 105 Paraserianthes lophanta 10 Neolitsea javanica 105 Pinanga coronata 10 Elaeocarpus angustifolius 75 Turpinia sphaerocarpa 10 Oreocnide scabra 75 Urophyllum arboreum 10 Elaeocarpus stipularis 60 Acer laurinum 5 Macropanax dispermum 60 Acronychia trifoliolata 5 Neolitsea cassiaefolia 60 Aglaia eclliptica 5 Syzygium rostratum 60 Alangium chinense 5 Cyathea latebrosa 55 Cryptocarya ferrea 5 Euonymus javanicus 55 Cyathea spinulosa 5 Phoebe grandis 55 Dacrycarpus imbricatus 5 Polyosma integrifolia 55 Engelhardtia spicata 5 Vernonia arborea 55 Ficus fistulosa 5 Flacourtia rukam 45 Ficus ribes 5 Macropanax concinnus 45 Gordonia excelsa 5 Ficus cuspidata 40 Lagerstroemia speciosa 5 Ardisia javanica 35 Laportea stimulans 5 Dysoxylum alliaceum 35 Magnolia lilifera 5 Astronia spectabilis 30 Prunus arborea 5 Trema orientalis 25 Rapanea hasseltii 5 Cyathea junghuhniana 20 Schefflera scandens 5 Lithocarpus pallidus 20 Syzygium antisepticum 5 Catanopsis acuminatissima 20 Toona sureni 5 Acronychia pedunculata 15 Weinmannia blumei 5 Leptospermum javanicum 15 Ziziphus angustifolia 5

55

APPENDIX 5

Estimation of carbon stock and biomass at several elevation on Mount Gede Pangrango National Park Brown (tons ha-1) Ketterings (tons ha-1) Chave (tons ha-1) Basuki (tons ha-1)

Site Elevation Carbon Carbon Carbon Carbon (masl) Biomassa stock Biomassa stock Biomassa stock Biomassa stock Gunung Gemuruh 2802 827.266 380.542 767.038 352.837 1027.545 472.671 801.447 368.666 Selabintana I 2601 1045.318 480.846 1014.460 466.651 1380.502 635.031 988.355 454.643 Selabintana II 2329 1299.031 597.554 1094.424 503.435 1412.520 649.759 954.979 439.290 Selabintana III 2075 900.818 414.376 733.331 337.332 920.893 423.611 663.435 305.180 Selabintana IV 1851 650.735 299.338 581.090 267.302 755.615 347.583 586.640 269.854 Selabintana V 1710 1179.498 542.569 1051.791 483.824 1316.959 605.801 944.984 434.693 PasirIpis 1355 508.341 233.837 429.923 197.765 566.545 260.610 471.895 217.072 Ciremes 1271 883.235 406.288 891.999 410.320 999.470 459.756 620.209 285.296 Ciberet 1079 526.309 242.102 374.714 172.368 418.200 192.372 340.912 156.820 average 868.950 399.717 770.974 354.648 977.583 449.688 708.095 325.724

maximum 1299.031 597.554 1094.424 503.435 1412.520 649.759 988.355 454.643 minimum 508.341 233.837 374.714 172.368 418.200 192.372 340.912 156.820

56

APPENDIX 6

Aboveground and soil carbon stock on study site

Aboveground biomass Contribution Soil carbon Contribution Total carbon stock

Site Elevation (tons ha-1) (%) (tons C ha-1) (%) (tons C ha-1)

(masl) Brown Ketterings Chave Basuki Brown Ketterings Chave Basuki Brown Ketterings Chave Basuki

Gunung Gemuruh 2802 380.542 352.837 472.671 368.666 99.998 99.998 99.999 99.998 0.007 0.002 380.549 352.844 472.677 368.672

Selabintana I 2601 480.846 466.651 635.031 454.643 99.998 99.998 99.999 99.998 0.007 0.001 480.854 466.659 635.038 454.651

Selabintana II 2329 597.554 503.435 649.759 439.29 99.999 99.999 99.999 99.999 0.006 0.001 597.56 503.441 649.765 439.296

Selabintana III 2075 414.376 337.332 423.611 305.18 99.998 99.998 99.998 99.998 0.007 0.002 414.383 337.339 423.618 305.187

Selabintana IV 1851 299.338 267.302 347.583 269.854 99.998 99.997 99.998 99.997 0.007 0.002 299.345 267.309 347.59 269.861

Selabintana V 1710 542.569 483.824 605.801 434.693 99.999 99.999 99.999 99.999 0.006 0.001 542.575 483.83 605.807 434.699

PasirIpis 1355 233.837 197.765 260.61 217.072 99.998 99.997 99.998 99.997 0.006 0.003 233.843 197.77 260.616 217.077

Ciremes 1271 406.288 410.32 459.756 285.296 99.999 99.999 100.000 99.999 0.002 0.001 406.29 410.322 459.758 285.298

Ciberet 1079 242.102 172.368 192.372 156.82 99.996 99.994 99.994 99.993 0.011 0.006 242.113 172.379 192.383 156.83

average 399.717 354.648 449.688 325.724 350.7095 338.0156 325.3217 312.6278 0.007 0.002 399.724 354.655 449.695 325.73

maximum 597.554 503.435 649.759 454.643 480.7455 452.5046 424.2637 396.0228 0.007 0.003 597.56 503.441 649.765 454.651

minimum 233.837 197.765 260.61 217.072 230.4585 231.7135 232.9685 234.2235 0.002 0.001 233.843 197.77 260.616 217.077

57

APPENDIX 7

Soil Texture, C organic and pH on study site Site Elevation Depth Texture C Class pH Category (m asl) (cm) (%)organic Gunung Gemuruh 2802 0-10 Sandy loam 16.09 Very high 5.1 Acid 10-20 Sandy loam 15.18 Very high 5.1 Acid Selabintana I 2601 0-10 Sandy loam 29.87 Very high 5.1 Acid 10-20 Sandy loam 15.44 Very high 5.1 Acid Selabintana II 2329 0-10 Sandy clay 12.31 Very high 4.8 Acid 10-20 Loamloam 8.18 Very high 4.9 Acid Selabintana III 2075 0-10 Loam 30.67 Very high 4.9 Acid 10-20 Sandy loam 15.61 Very high 4.8 Acid Selabintana IV 1851 0-10 Sandy loam 29.23 Very high 4.8 Acid 10-20 Sandy loam 20.52 Very high 5 Acid Selabintana V 1710 0-10 Loam 20.47 Very high 4.7 Acid 10-20 Loam 17.5 Very high 4.9 Acid Pasir Ipis 1355 0-10 Loam 20.51 Very high 4.9 Acid 10-20 Clay loam 12.38 Very high 4.8 Acid Ciremes 1271 0-10 Sandy clay 9.46 Very high 5.3 Acid 10-20 Claloamy loam 4.53 High 4.7 Acid Ciberet 1079 0-10 Loam 31.86 Very high 5.8 Moderate 10-20 Sandy loam 12.7 Very high 5.2 Aacicidd Note: Analysis is conducted by Indonesian Soil Reseach Institute, Ministry of Agriculture, Republic of Indonesia

58

APPENDIX 8

Microclimate on study site Relative humidity Soil Elevation Slope Temperature (C) (%) moisture Site (masl) (%) Max Min Max Min (%) Gunung Gemuruh 2802 67.5 23.8 15.6 53 36 47.3 Selabintana I 2601 65 23.8 8.4 64 36 54.5 Selabintana II 2329 52 23.8 8.4 69.3 36 67.3 Selabintana III 2075 44 23.8 10.9 76.5 45.8 55.3 Selabintana IV 1851 26 24.8 17.8 78 60 54 Selabintana V 1710 35 24.8 17.7 89 60 50 Pasir Ipis 1355 35 24.8 17.7 89.8 60 58

Ciremes 1271 33 22* 18* 90* 80* 47.13

Ciberet 1079 13 22* 18* 90* 80* 67.6 *: Mount Gede Pangrango National Park (2011)

59